Validation / Papers / Rouault 2026

Rouault and the GDAL contributors: GDAL and OGR

Geospatial · tool tutorial or software test data · GDAL (command-line programs), through the gdal adapter

How to read this page

In this validation, a script plays the scientist. It gives the answers that we wrote before the run, from the methods of the paper. The run is one sample: another run can give different steps and numbers. The model is the AI. The harness is Cuvette, the software around the model: it runs the programs and records each step. A tool call is a request from the model to run one program step. The session record is the log of each message and each step. The claim check is a script that finds each number of the final answer in the step results. The review is a set of fixed rule checks plus a second AI model, the referee, that reads the record. A deviation is a request from the model for a setting that differs from the choice of the scientist. Each Claude model did 3 runs of this paper. This page shows run 3 of each Claude model and the one run of qwen3:8b. The table of values says how many of the Claude runs match.

Opus: 14 of 14 values match, 14 of 14 correct in the final answer. All 3 runs: 14 of 14 values match. Sonnet: 14 of 14 values match, 14 of 14 correct in the final answer. All 3 runs: 14 of 14 values match. Haiku: 14 of 14 values match, 14 of 14 correct in the final answer. All 3 runs: 14 of 14 values match. qwen3:8b: 8 of 14 values match, 8 of 14 correct in the final answer.

The paper

Rouault E, Warmerdam F, Schwehr K, et al.. GDAL. Zenodo software record, version 3.13.3 (2026). doi:10.5281/zenodo.5884351

Related sources:

GDAL test suite (autotest), test_gdalinfo.py. Its test test_gdalinfo_stac_json checks the size, projection and band statistics of byte.tif. link

What it measured

GDAL is a library and a set of command-line programs that read, write and transform geospatial raster and vector data. It has no single paper with result tables. Its test suite runs gdalinfo on a small test raster, byte.tif, and checks the size, the map projection and the band statistics. We add a warp to latitude and longitude and two zones that we drew. These steps test the choice of projection, resampling and the rule for pixels that a zone edge cuts.

Data

GDAL test raster byte.tif, plus two square zones that we drew. Size: 736 bytes, 20 x 20 pixels (byte.tif). 472 bytes, two polygons (zones.geojson)..

License: GDAL and its test data are MIT licensed. The zone file is part of this validation.

Data source

The instruction

A script sent this message as the scientist. The file paths point to the fetched data.

ScientistThe raster is {data}/rouault-gdal-autotest/byte.tif and the two squares are in {data}/rouault-gdal-autotest/zones.geojson . The name of each square is in the field id. 1. I have a small satellite-style image of a patch of land. How many pixels wide and high is it, which map projection does it use, and how large is one pixel on the ground? 2. What are the lowest, highest and average pixel values, and the standard deviation? 3. Convert the image to latitude and longitude. How many pixels wide and high is the new image? 4. I drew two squares on the map. What is the average pixel value inside the first one, and how many pixels does it cover? 5. The second square cuts through pixels. How many pixels and what average do I get if I count only pixels whose center is inside the square?

The same request in the words of the paper's method:

I have a small satellite-style image of a patch of land and two squares that I drew on the map. Give me the image size in pixels, its map projection and the ground size of one pixel. Give me the lowest, highest and mean pixel values and the standard deviation. Convert the image to latitude and longitude and give me the new size. Give me the mean value and the pixel count inside the first square. For the second square, count only the pixels whose centers are inside it.

Basis: Questions 1 and 2 follow the gdalinfo checks on byte.tif in the GDAL test suite. Questions 3 to 5 use gdalwarp and zonal statistics on zones that we added.

Results

Match: a number in the session record is inside the tolerance of the known value. In the final answer: the model also stated the value in its final answer. For a Claude model, each cell shows the run that this page shows. If the three runs differ, the cell also says in how many runs the value matches.

**Table 1 |** Known values and the value of each model.
Value	Known value	Tolerance	Opus	Sonnet	Haiku	qwen3:8b
`width`Raster width in pixels Source of the known value Printed in the official tutorialGDAL test suite, test_gdalinfo.py, test_gdalinfo_stac_json. The test checks a raster shape of 20 by 20 pixels.	20	exact	20 matchIn the final answer: yes (20)Log: n1 `raster_info` `metrics.width`, entry 11; the final answer, entry 84	20 matchIn the final answer: yes (20)Log: n1 `raster_info` `metrics.width`, entry 17; the final answer, entry 66	20 matchIn the final answer: yes (20)Log: n1 `raster_info` `metrics.width`, entry 11; the final answer, entry 71	20 matchIn the final answer: yes (20)Log: n1 `raster_info` `metrics.width`, entry 9; the final answer, entry 16
`height`Raster height in pixels Source of the known value Printed in the official tutorialGDAL test suite, test_gdalinfo.py, test_gdalinfo_stac_json. The test checks a raster shape of 20 by 20 pixels.	20	exact	20 matchIn the final answer: yes (20)Log: n1 `raster_info` `metrics.width`, entry 11; the final answer, entry 84	20 matchIn the final answer: yes (20)Log: n1 `raster_info` `metrics.width`, entry 17; the final answer, entry 66	20 matchIn the final answer: yes (20)Log: n1 `raster_info` `metrics.width`, entry 11; the final answer, entry 71	20 matchIn the final answer: yes (20)Log: n1 `raster_info` `metrics.width`, entry 9; the final answer, entry 16
`epsg`EPSG code of the raster Source of the known value Printed in the official tutorialGDAL test suite, test_gdalinfo.py, test_gdalinfo_stac_json. The test checks the code EPSG:26711 (North American Datum 1927, Universal Transverse Mercator zone 11 north).	26711	exact	26711 matchIn the final answer: yes (26711)Log: n1 `raster_info` `metrics.epsg`, entry 11; the final answer, entry 84	26711 matchIn the final answer: yes (26711)Log: n1 `raster_info` `metrics.epsg`, entry 17; the final answer, entry 66	26711 matchIn the final answer: yes (26711)Log: n1 `raster_info` `metrics.epsg`, entry 11; the final answer, entry 71	26711 matchIn the final answer: yes (26711)Log: n1 `raster_info` `metrics.epsg`, entry 9; the final answer, entry 16
`pixel_size_m`Pixel size in metres Source of the known value Printed in the official tutorialGDAL test suite, test_gdalinfo.py, test_gdalinfo_stac_json. The geotransform in the test has a pixel size of 60 m.	60	exact	60 matchIn the final answer: yes (60)Log: n1 `raster_info` `metrics.pixel_x`, entry 11; the final answer, entry 84	60 matchIn the final answer: yes (60)Log: n1 `raster_info` `metrics.pixel_x`, entry 17; the final answer, entry 66	60 matchIn the final answer: yes (60)Log: n1 `raster_info` `metrics.pixel_x`, entry 11; the final answer, entry 71	60 matchIn the final answer: yes (60)Log: n1 `raster_info` `metrics.pixel_x`, entry 9; the final answer, entry 16
`band_min`Band minimum Source of the known value Printed in the official tutorialGDAL test suite, test_gdalinfo.py, test_gdalinfo_stac_json. The test checks a minimum of 74.	74	exact	74 matchIn the final answer: yes (74)Log: n1 `raster_info` `metrics.min`, entry 11; the final answer, entry 84	74 matchIn the final answer: yes (74)Log: n1 `raster_info` `metrics.min`, entry 17; the final answer, entry 66	74 matchIn the final answer: yes (74)Log: n1 `raster_info` `metrics.min`, entry 11; the final answer, entry 71	74 matchIn the final answer: yes (74)Log: n1 `raster_info` `metrics.min`, entry 9; the final answer, entry 16
`band_max`Band maximum Source of the known value Printed in the official tutorialGDAL test suite, test_gdalinfo.py, test_gdalinfo_stac_json. The test checks a maximum of 255.	255	exact	255 matchIn the final answer: yes (255)Log: n1 `raster_info` `metrics.max`, entry 11; the final answer, entry 84	255 matchIn the final answer: yes (255)Log: n1 `raster_info` `metrics.max`, entry 17; the final answer, entry 66	255 matchIn the final answer: yes (255)Log: n1 `raster_info` `metrics.max`, entry 11; the final answer, entry 71	255 matchIn the final answer: yes (255)Log: n1 `raster_info` `metrics.max`, entry 9; the final answer, entry 16
`band_mean`Band mean Source of the known value Printed in the official tutorialGDAL test suite, test_gdalinfo.py, test_gdalinfo_stac_json. The test checks a mean of 126.765.	126.765	± 0.01	126.765 matchIn the final answer: yes (126.765)Log: n1 `raster_info` `metrics.mean`, entry 11; the final answer, entry 84	126.765 matchIn the final answer: yes (126.765)Log: n1 `raster_info` `metrics.mean`, entry 17; the final answer, entry 66	126.765 matchIn the final answer: yes (126.765)Log: n1 `raster_info` `metrics.mean`, entry 11; the final answer, entry 71	126.765 matchIn the final answer: yes (126.765)Log: n1 `raster_info` `metrics.mean`, entry 9; the final answer, entry 16
`band_std`Band standard deviation Source of the known value Printed in the official tutorialGDAL test suite, test_gdalinfo.py, test_gdalinfo_stac_json. The test checks 22.928, the population standard deviation. The sample standard deviation is 22.957, which is also inside the tolerance.	22.93	± 0.05	22.928 matchIn the final answer: yes (22.928)Log: n1 `raster_info` `metrics.stddev`, entry 11; the final answer, entry 84	22.928 matchIn the final answer: yes (22.928)Log: n1 `raster_info` `metrics.stddev`, entry 17; the final answer, entry 66	22.928 matchIn the final answer: yes (22.928)Log: n1 `raster_info` `metrics.stddev`, entry 11; the final answer, entry 71	22.928 matchIn the final answer: yes (22.928)Log: n1 `raster_info` `metrics.stddev`, entry 9; the final answer, entry 16
`warp_width`Width after warp to EPSG:4326 Source of the known value We calculated it with GDAL 3.13.3 gdalwarp, checked with pyprojNot in the test suite. We ran the warp with nearest neighbour and the default pixel size.	22	exact	22 matchIn the final answer: yes (22)Log: n3 `reproject_raster` `metrics.width`, entry 23; the final answer, entry 84	22 matchIn the final answer: yes (22)Log: n3 `reproject_raster` `metrics.width`, entry 28; the final answer, entry 66	22 matchIn the final answer: yes (22)Log: n3 `reproject_raster` `metrics.width`, entry 22; the final answer, entry 71	22.928 no matchIn the final answer: no (22.928)Log: n1 `raster_info` `metrics.stddev`, entry 9; the final answer, entry 16
`warp_height`Height after warp to EPSG:4326 Source of the known value We calculated it with GDAL 3.13.3 gdalwarp, checked with pyprojNot in the test suite. We ran the warp with nearest neighbour and the default pixel size.	18	exact	18 matchIn the final answer: yes (18)Log: n3 `reproject_raster` `metrics.height`, entry 23; the final answer, entry 84	18 matchIn the final answer: yes (18)Log: n3 `reproject_raster` `metrics.height`, entry 28; the final answer, entry 66	18 matchIn the final answer: yes (18)Log: n3 `reproject_raster` `metrics.height`, entry 22; the final answer, entry 71	20 no matchIn the final answer: no (20)Log: n1 `raster_info` `metrics.width`, entry 9; the final answer, entry 16
`zone_a_pixels`Zone A pixel count Source of the known value We calculated it with numpy and shapelyNot in a source. Zone A is a square that we drew on the pixel grid.	100	exact	100 matchIn the final answer: yes (100)Log: n4 `zonal_stats` `metrics.count`, entry 26; the final answer, entry 84	100 matchIn the final answer: yes (100)Log: n4 `zonal_stats` `metrics.count`, entry 31; the final answer, entry 66	100 matchIn the final answer: yes (100)Log: n4 `zonal_stats` `metrics.count`, entry 25; the final answer, entry 71	74 no matchIn the final answer: no (74)Log: n1 `raster_info` `metrics.min`, entry 9; the final answer, entry 16
`zone_a_mean`Zone A mean Source of the known value We calculated it with numpy and shapelyNot in a source. Zone A is a square that we drew on the pixel grid.	129.5	± 0.01	129.5 matchIn the final answer: yes (129.5)Log: n4 `zonal_stats` `metrics.mean`, entry 26; the final answer, entry 84	129.5 matchIn the final answer: yes (129.5)Log: n4 `zonal_stats` `metrics.mean`, entry 31; the final answer, entry 66	129.5 matchIn the final answer: yes (129.5)Log: n4 `zonal_stats` `metrics.mean`, entry 25; the final answer, entry 71	126.765 no matchIn the final answer: no (126.765)Log: n1 `raster_info` `metrics.mean`, entry 9; the final answer, entry 16
`zone_b_pixels_centers`Zone B pixel count, pixel centers Source of the known value We calculated it with numpy and shapelyNot in a source. Zone B is offset by 40 m from the grid. With all touched pixels the count is 56, not 42.	42	exact	42 matchIn the final answer: yes (42)Log: n4 `zonal_stats` `table.rows[1][1]`, entry 26; the final answer, entry 84	42 matchIn the final answer: yes (42)Log: n4 `zonal_stats` `table.rows[1][1]`, entry 31; the final answer, entry 66	42 matchIn the final answer: yes (42)Log: n4 `zonal_stats` `table.rows[1][1]`, entry 25; the final answer, entry 71	60 no matchIn the final answer: no (60)Log: n1 `raster_info` `metrics.pixel_x`, entry 9; the final answer, entry 16
`zone_b_mean_centers`Zone B mean, pixel centers Source of the known value We calculated it with numpy and shapelyNot in a source. With all touched pixels the mean is 134.857, not 135.0.	135	± 0.01	135 matchIn the final answer: yes (135)Log: n4 `zonal_stats` `table.rows[1][4]`, entry 26; the final answer, entry 84	135 matchIn the final answer: yes (135)Log: n4 `zonal_stats` `table.rows[1][4]`, entry 31; the final answer, entry 66	135 matchIn the final answer: yes (135)Log: n4 `zonal_stats` `table.rows[1][4]`, entry 25; the final answer, entry 71	126.765 no matchIn the final answer: no (126.765)Log: n1 `raster_info` `metrics.mean`, entry 9; the final answer, entry 16

Session records

Session record, Opus, run 3 of 3

Every message, decision, step and result of this run, one JSON object for each log entry.

A compressed log file (JSON Lines, gzip). The record shows file paths as {data}, {work} and similar names. · 21 KB

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Session record, Sonnet, run 3 of 3

Every message, decision, step and result of this run, one JSON object for each log entry.

A compressed log file (JSON Lines, gzip). The record shows file paths as {data}, {work} and similar names. · 17 KB

Download

Session record, Haiku, run 3 of 3

Every message, decision, step and result of this run, one JSON object for each log entry.

A compressed log file (JSON Lines, gzip). The record shows file paths as {data}, {work} and similar names. · 30 KB

Download

Session record, qwen3:8b

Every message, decision, step and result of this run, one JSON object for each log entry.

A compressed log file (JSON Lines, gzip). The record shows file paths as {data}, {work} and similar names. · 3 KB

Download

Model in this columnOpus · claude-opus-5-5 · run 3 of 3 shown 14 of 14 values match, 14 of 14 correct in the final answer

The session

This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.

setup The decision record starts with these values. A script set them before the first message, from the answers that we wrote before the run.

Target coordinate reference system (CRS): EPSG:4326Source in the tutorial or test suite: The request asks for latitude and longitude. The test suite has no warp step for this file, so we chose this CRS.
Resampling method: nearSource in the tutorial or test suite: Not in a source. We use the gdalwarp default. The warp is only a size check.
Nodata value: Source in the tutorial or test suite: The file has no nodata value. We set none.
Output pixel size: Source in the tutorial or test suite: Not in a source. We use the gdalwarp default.
Pixels that a zone edge cuts: centersSource in the tutorial or test suite: The request states this rule for the second square. It is also the default in rasterstats.

harness note A note from the harness to the model. The scientist did not write it.

- Setup values:
Geometry:
- Target coordinate reference system (target_crs): EPSG:4326
- Resampling method (resampling): near
- Output pixel size (pixel_size): 
Data:
- Nodata value (nodata): 
Zonal statistics:
- Pixels that a zone edge cuts (partial_pixels): centers
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

Model thinking, summary: I should start by checking the raster_info and vector_info skills in parallel to be efficient.

Model

I will read the raster and the polygon file first. Then I will reproject the raster and take zonal statistics.

The model calls raster_info (adapter gdal).

step n1 raster_info adapter gdal 0.1.1, GDAL 3.13.3

20 x 20 pixels, EPSG:26711, mean 126.765, stddev 22.928.

Input file: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192.

Arguments

path	`{data}/rouault-gdal-autotest/byte.tif`
band	`1`

Tool output

{
 "ok": true,
 "metrics": {
  "width": 20,
  "height": 20,
  "n_bands": 1,
  "pixel_x": 60,
  "pixel_y": 60,
  "origin_x": 440720,
  "origin_y": 3751320,
  "epsg": 26711,
  "min": 74,
  "max": 255,
  "mean": 126.765,
  "stddev": 22.928
 },
 "data": {
  "crs_name": "NAD27 / UTM zone 11N",
  "driver": "GTiff",
  "type": "Byte",
  "corner_ul": [
   440720,
   3751320
  ],
  "corner_lr": [
   441920,
   3750120
  ]
 },
 "manualRoute": "gdalinfo -stats {data}/rouault-gdal-autotest/byte.tif"
}

The model calls vector_info (adapter gdal).

step n2 vector_info adapter gdal 0.1.1, GDAL 3.13.3

2 features, EPSG:26711.

Input file: {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Arguments

path	`{data}/rouault-gdal-autotest/zones.geojson`

Tool output

{
 "ok": true,
 "metrics": {
  "n_features": 2,
  "n_layers": 1,
  "n_fields": 1,
  "epsg": 26711,
  "xmin": 440720,
  "ymin": 3750720,
  "xmax": 441320,
  "ymax": 3751320
 },
 "data": {
  "layer": "zones",
  "geometry_type": "Polygon",
  "fields": [
   "id"
  ]
 },
 "manualRoute": "ogrinfo -json -so -al {data}/rouault-gdal-autotest/zones.geojson"
}

The model reads the method notes zonal-statistics.

Model thinking, summary: Both datasets are in EPSG:26711 with no nodata reported for the raster. I'll proceed with reprojecting to EPSG:4326 using nearest resampling and run zonal stats on the original data with centers, then compare against all_touched as a check using compare_options.

Model thinking, summary: Both the raster and squares share EPSG:26711, so no reprojection is needed for zonal stats. I'll now reproject the raster to EPSG:4326 using nearest-neighbour, and compute zonal statistics on the original raster with the pixel-center rule.

The model calls reproject_raster (adapter gdal).

step n3 reproject_raster adapter gdal 0.1.1, GDAL 3.13.3

Reproject a raster finished. width=22, height=18, n_bands=1, pixel_x=0.00059812.

Decisions applied: Target coordinate reference system (CRS) = EPSG:4326; Resampling method = near.

Input file: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192.

Outputs: result (4e4c38b9dbb7).

Arguments

path	`{data}/rouault-gdal-autotest/byte.tif`
target_crs	`EPSG:4326`
resampling	`near`

Tool output

{
 "ok": true,
 "metrics": {
  "width": 22,
  "height": 18,
  "n_bands": 1,
  "pixel_x": 0.0005981204678461,
  "pixel_y": 0.0005981204678461,
  "origin_x": -117.64205396140792,
  "origin_y": 33.902434573500166,
  "epsg": 4326,
  "min": 74,
  "max": 255,
  "mean": 126.551,
  "stddev": 21.882,
  "src_width": 20,
  "src_height": 20
 },
 "data": {
  "crs_name": "WGS 84",
  "driver": "GTiff",
  "type": "Byte",
  "corner_ul": [
   -117.642054,
   33.9024346
  ],
  "corner_lr": [
   -117.6288953,
   33.8916684
  ]
 },
 "manualRoute": "gdalwarp -overwrite -of GTiff -t_srs EPSG:4326 -r near {data}/rouault-gdal-autotest/byte.tif {work}/reproject_raster-1/result.tif"
}

The model calls zonal_stats (adapter gdal).

step n4 zonal_stats adapter gdal 0.1.1, GDAL 3.13.3

2 zone(s). First zone: 100 pixels, mean 129.5.

Decisions applied: Pixels that a zone edge cuts = centers.

Input files: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192; {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Outputs: zonal_stats.csv (a0eef9b5a536).

Arguments

raster	`{data}/rouault-gdal-autotest/byte.tif`
zones	`{data}/rouault-gdal-autotest/zones.geojson`
id_field	`id`
pixels	`centers`

Tool output

{
 "ok": true,
 "metrics": {
  "n_zones": 2,
  "count": 100,
  "min": 99,
  "max": 197,
  "mean": 129.5,
  "sum": 12950,
  "std": 17.90335164152232,
  "median": 132
 },
 "data": {
  "zones": [
   {
    "zone": "A",
    "count": 100,
    "min": 99,
    "max": 197,
    "mean": 129.5,
    "sum": 12950,
    "std": 17.90335164152232,
    "median": 132
   },
   {
    "zone": "B",
    "count": 42,
    "min": 99,
    "max": 173,
    "mean": 135,
    "sum": 5670,
    "std": 17.210185245675778,
    "median": 132
   }
  ],
  "pixels": "centers",
  "nodata": null,
  "raster_epsg": 26711
 },
 "manualRoute": "rasterstats.zonal_stats('{data}/rouault-gdal-autotest/zones.geojson', '{data}/rouault-gdal-autotest/byte.tif', all_touched=False, nodata=None)",
 "table": {
  "columns": [
   "zone",
   "count",
   "min",
   "max",
   "mean",
   "sum",
   "std",
   "median"
  ],
  "rows": [
   [
    "A",
    100,
    99,
    197,
    129.5,
    12950,
    17.90335164152232,
    132
   ],
   [
    "B",
    42,
    99,
    173,
    135,
    5670,
    17.210185245675778,
    132
   ]
  ],
  "n_rows": 2,
  "path": "{work}/zonal_stats-1/zonal_stats.csv"
 },
 "outputs": [
  {
   "path": "{work}/zonal_stats-1/zonal_stats.csv",
   "kind": "table",
   "name": "zonal_stats.csv"
  }
 ]
}

Model thinking, summary: Since Square B cuts pixels, I should verify this by comparing results with all_touched enabled, using the partial_pixels key in compare_options, and plotting the map to double-check the outlines.

Model

Square B cuts through pixels. As a check only, I compare the all-touched rule. I also draw the squares on the raster to make sure that they sit on the image.

The model calls compare_options.

comparison run n5 zonal_stats adapter gdal 0.1.1, GDAL 3.13.3

2 zone(s). First zone: 100 pixels, mean 129.5.

Input files: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192; {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Outputs: zonal_stats.csv (7a4a4ded81aa).

Arguments

raster	`{data}/rouault-gdal-autotest/byte.tif`
zones	`{data}/rouault-gdal-autotest/zones.geojson`
id_field	`id`
pixels	`all_touched`

Tool output

{
 "ok": true,
 "metrics": {
  "n_zones": 2,
  "count": 100,
  "min": 99,
  "max": 197,
  "mean": 129.5,
  "sum": 12950,
  "std": 17.90335164152232,
  "median": 132
 },
 "data": {
  "zones": [
   {
    "zone": "A",
    "count": 100,
    "min": 99,
    "max": 197,
    "mean": 129.5,
    "sum": 12950,
    "std": 17.90335164152232,
    "median": 132
   },
   {
    "zone": "B",
    "count": 56,
    "min": 99,
    "max": 197,
    "mean": 134.85714285714286,
    "sum": 7552,
    "std": 19.986985561528147,
    "median": 132
   }
  ],
  "pixels": "all_touched",
  "nodata": null,
  "raster_epsg": 26711
 },
 "manualRoute": "rasterstats.zonal_stats('{data}/rouault-gdal-autotest/zones.geojson', '{data}/rouault-gdal-autotest/byte.tif', all_touched=True, nodata=None)",
 "table": {
  "columns": [
   "zone",
   "count",
   "min",
   "max",
   "mean",
   "sum",
   "std",
   "median"
  ],
  "rows": [
   [
    "A",
    100,
    99,
    197,
    129.5,
    12950,
    17.90335164152232,
    132
   ],
   [
    "B",
    56,
    99,
    197,
    134.85714285714286,
    7552,
    19.986985561528147,
    132
   ]
  ],
  "n_rows": 2,
  "path": "{work}/zonal_stats-2/zonal_stats.csv"
 },
 "outputs": [
  {
   "path": "{work}/zonal_stats-2/zonal_stats.csv",
   "kind": "table",
   "name": "zonal_stats.csv"
  }
 ]
}

comparison Comparison runs for Pixels that a zone edge cuts. The record keeps the scientist's choice.

Pixels that a zone edge cuts  count  mean   Result
all_touched                   100    129.5  ok

Result of compare_options: Compared 1 values of Pixels that a zone edge cuts as comparison runs of the last step, zonal_stats (id_field=id). Only that step was repeated. To compare anothe ...

Compared 1 values of Pixels that a zone edge cuts as comparison runs of the last step, zonal_stats (id_field=id). Only that step was repeated. To compare another step, run it again first. The record still says "centers".
Pixels that a zone edge cuts  count  mean   Result
all_touched                   100    129.5  ok

The model calls plot_map (adapter gdal).

step n6 plot_map adapter gdal 0.1.1, GDAL 3.13.3

Map of 400 valid pixels.

Input files: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192; {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Outputs: result (e7754e92bc5e).

Arguments

raster	`{data}/rouault-gdal-autotest/byte.tif`
zones	`{data}/rouault-gdal-autotest/zones.geojson`
title	`byte.tif (EPSG:26711) with squares A and B`
colormap	`gray`

Tool output

{
 "ok": true,
 "metrics": {
  "width_px": 20,
  "height_px": 20,
  "valid_pixels": 400
 },
 "data": {
  "colormap": "gray"
 },
 "manualRoute": "Open {data}/rouault-gdal-autotest/byte.tif in QGIS and add {data}/rouault-gdal-autotest/zones.geojson as an outline layer",
 "outputs": [
  {
   "path": "{work}/plot_map-1/result.png",
   "kind": "image",
   "name": "map.png"
  }
 ]
}

Model thinking, summary: The compare table only shows zone A, so I need zone B's all_touched value too, but running zonal_stats directly would count as a deviation from the harness. Since no CSV of the comparison run is available, I'm considering whether I can derive the expected value for B from its geometry instead.

Model

The comparison result shows only the first square. I look for the comparison table to find the numbers for square B.

The model calls list_dir.

Result of list_dir: 12 entries in {work}.

- ._plot_map-1  4096
- ._raster_info-1  4096
- ._reproject_raster-1  4096
- ._vector_info-1  4096
- ._zonal_stats-1  4096
- ._zonal_stats-2  4096
d plot_map-1
d raster_info-1
d reproject_raster-1
d vector_info-1
d zonal_stats-1
d zonal_stats-2

The model calls read_file.

Result of read_file: Read 164 of 164 bytes from {work}/zonal_stats-2/zonal_stats.csv.

zone,count,min,max,mean,sum,std,median
A,100,99.0,197.0,129.5,12950.0,17.90335164152232,132.0
B,56,99.0,197.0,134.85714285714286,7552.0,19.986985561528147,132.0

Model thinking, summary: For B, the difference is 56 minus 42, which gives 14 pixels as the mean difference.

The model runs calculate.

{"items":[{"name":"B_extra_pixels_all_touched","expression":"56 - 42"},{"name":"B_mean_difference","expression":"134.85714285714286 - 135.0"}],"purpose":"Show how much square B changes between the center rule and the all-touched rule."}

step n7 calculate

Calculated 2 values.

Arguments

items [{"name":"B_extra_pixels_all_touched","expression":"56 - 42"},{"name":"B_mean_difference","expression":"134.85714285714286 - 135.0"}]

Tool output

B_extra_pixels_all_touched = 56 - 42 = 14
B_mean_difference = 134.85714285714286 - 135.0 = -0.1428571429

Model Answer draft 1. The claim check and the review read it.

I read the image and the squares, converted the image to latitude and longitude, and measured both squares on the original image.

1. Size, projection and pixel size (from `raster_info`, which runs `gdalinfo -stats`)

Size: 20 pixels wide and 20 pixels high. The image has one band of type Byte.
Projection: EPSG:26711, which is NAD27 / UTM zone 11N.
Pixel size on the ground: 60 m × 60 m.
Extent: the upper-left corner is at 440720 m E, 3751320 m N. The lower-right corner is at 441920 m E, 3750120 m N.

2. Pixel values (from `raster_info`)

Lowest: 74
Highest: 255
Average: 126.765
Standard deviation: 22.928

The file has no nodata value, so these statistics use all 400 pixels.

3. Image in latitude and longitude (from `reproject_raster`, which runs `gdalwarp`)

Settings: target EPSG:4326 (WGS 84) with nearest-neighbour resampling. The pixel size was empty, so GDAL chose it.
New size: 22 pixels wide and 18 pixels high.
New pixel size: 0.00059812 degrees. This is in degrees, not metres, so I do not give a ground size for it.
Statistics after the conversion:
Lowest 74 and highest 255, the same as before.
Average 126.551 and standard deviation 21.882, which differ from the original. The resampling makes this change.
Output file: reproject_raster-1/result.tif

4. First square, A (from `zonal_stats`, which uses rasterstats)

I took the statistics on the original raster, which uses the same EPSG:26711 as the squares. I counted pixels whose center is inside the square, and no nodata value was set.

Average pixel value: 129.5
Pixels covered: 100
Other values: lowest 99, highest 197, sum 12950, standard deviation 17.90.

5. Second square, B, pixel centers only (from `zonal_stats`, same run)

Pixels covered: 42
Average pixel value: 135.0
Other values: lowest 99, highest 173, sum 5670, standard deviation 17.21.

Check: all touched pixels (comparison run only)

This check did not change your recorded setting, which is still pixel centers.

Square A: 100 pixels, average 129.5. The edges of A follow the pixel edges, so both rules give the same result.
Square B: 56 pixels, average 134.857. This is 14 more pixels than the center rule, and the average is 0.143 lower (from calculate).

The map (plot_map-1/result.png) shows both squares inside the image, in the upper-left part. Square B's edges cut through pixels.

What is uncertain

Square B is small, so the 14 edge pixels change its count by a large fraction. Its average changes very little.
A count tells you the number of pixels, not the exact area of the square. Square B's true area is not a whole number of pixels.
You did not set a nodata value. If a value such as 0 or 255 must mean "no data", all the statistics above must be run again.

Your decisions

Settings I used: target EPSG:4326, nearest-neighbour resampling, no pixel size, no nodata value, and the pixel-center rule.
Open question: please confirm that the file has no nodata value.

claim check 45 numbers in the answer, 1 number without a source in the record.

The review found 2 problems. The model corrects the answer before the scientist sees it.

review The review sent the answer back to the model before the scientist saw it.

- The answer uses 56, 134.857 from a comparison run of another option (partial_pixels), not from the setting that was chosen. Use the result of the chosen setting, or say clearly that the number is from the comparison.
- 1 number in the answer match no logged tool result: 0.143. Run the tool that measures each number, or remove the number, or say how you derived it from logged results.
Fix these problems. Then give the answer again.

Model thinking, summary: I should get a clean rounded value for that -0.1428571429 result, so let me run it through calculate to round it properly before reporting.

Model

I round the difference between the two averages for square B with a tool. This makes the reported number trace back to a logged result.

The model runs calculate.

{"items":[{"name":"B_mean_difference_rounded","expression":"round(134.85714285714286 - 135.0, 3)"}],"purpose":"Round the change in square B's average between the two pixel rules to three decimals."}

step n8 calculate

Calculated 1 value.

Arguments

items [{"name":"B_mean_difference_rounded","expression":"round(134.85714285714286 - 135.0, 3)"}]

Tool output

B_mean_difference_rounded = round(134.85714285714286 - 135.0, 3) = -0.143

Model The model writes its answer. The claim check reads it. See the final answer.

claim check 45 numbers in the answer, 0 numbers without a source in the record.

Saved: results.xlsx (6 sheets), 2 figures in figures/. Folder: {work}

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

I read the image and the squares, converted the image to latitude and longitude, and measured both squares on the original image. Every result below uses your recorded settings, except one check that is clearly marked.

1. Size, projection and pixel size (from `raster_info`, which runs `gdalinfo -stats`)

Size: 20 pixels wide and 20 pixels high. The image has one band of type Byte.
Projection: EPSG:26711, which is NAD27 / UTM zone 11N.
Pixel size on the ground: 60 m × 60 m.
Extent: the upper-left corner is at 440720 m E, 3751320 m N. The lower-right corner is at 441920 m E, 3750120 m N.

2. Pixel values (from `raster_info`)

Lowest: 74
Highest: 255
Average: 126.765
Standard deviation: 22.928

The file has no nodata value, so these statistics use all 400 pixels.

3. Image in latitude and longitude (from `reproject_raster`, which runs `gdalwarp`)

Settings: target EPSG:4326 (WGS 84) with nearest-neighbour resampling. The pixel size was empty, so GDAL chose it.
New size: 22 pixels wide and 18 pixels high.
New pixel size: 0.00059812 degrees. This is in degrees, not metres, so I do not give a ground size for it.
Statistics after the conversion:
Lowest 74 and highest 255, the same as before.
Average 126.551 and standard deviation 21.882, which differ from the original. The resampling makes this change.
Output file: reproject_raster-1/result.tif

4. First square, A (from `zonal_stats`, which uses rasterstats)

I took the statistics on the original raster, which uses the same EPSG:26711 as the squares. I used your recorded pixel-center rule, and no nodata value was set.

Average pixel value: 129.5
Pixels covered: 100
Other values: lowest 99, highest 197, sum 12950, standard deviation 17.90.

5. Second square, B, pixel centers only (from `zonal_stats`, same run)

Pixels covered: 42
Average pixel value: 135.0
Other values: lowest 99, highest 173, sum 5670, standard deviation 17.21.

Check only, not a result: all touched pixels

These numbers come from a comparison run (compare_options, table zonal_stats-2/zonal_stats.csv). They do not replace the results above. Your recorded setting is still pixel centers.

Square A: 100 pixels, average 129.5. The edges of A follow the pixel edges, so both rules give the same result.
Square B: 56 pixels, average 134.857.
Change for square B (from calculate, center result subtracted from the all-touched result):
14 more pixels.
The average changes by -0.143.

The map (plot_map-1/result.png) shows both squares inside the image, in the upper-left part. Square B's edges cut through pixels.

What is uncertain

Square B is small, so the pixel rule changes its count by a large fraction. Its average changes very little.
A count tells you the number of pixels, not the exact area of the square. Square B's true area is not a whole number of pixels.
You did not set a nodata value. If a value such as 0 or 255 must mean "no data", all the statistics above must be run again.

Your decisions

Settings I used: target EPSG:4326, nearest-neighbour resampling, no pixel size, no nodata value, and the pixel-center rule.
Open question: please confirm that the file has no nodata value.

Settings and step results that the answer does not mention. The harness adds them.

Settings used, from the decision record: Target coordinate reference system: EPSG:4326 · Resampling method: near · Pixels that a zone edge cuts: centers.

Checks

Review findings

The review recorded 6 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

**Table 2 |** Review findings, Opus run.
Severity	From	Finding	Shown with the final answer
error	rulenumber_from_comparison	The answer uses 56, 134.857 from a comparison run of another option (partial_pixels), not from the setting that was chosen. Use the result of the chosen setting, or say clearly that the number is from the comparison.	yes
info	ruletext_style	The answer breaks the text rules (ASD-STE100) in 2 places. Sentence 25 uses the passive voice: "was set". Use the active voice. Sentence 48 uses the passive voice: "be run". Use the active voice.	yes
warning	referee model	The answer says that the file has no nodata value. No logged step reports the nodata value of byte.tif. The raster_info metrics have no nodata field. The zonal run only passed nodata=None. The answer also asks the scientist to confirm this, so the first statement is stronger than the evidence.	yes
warning	referee model	The answer says that the image band has type Byte. No logged result gives the data type. raster_info reports only the band count, so the data type must come from a tool result or be left out.	yes
info	referee model	The answer names nearest-neighbour resampling for the EPSG:4326 output but gives no reason for it. The standard asks for the method and the reason.	yes
info	referee model	The answer says that the edges of square A follow the pixel edges and that square B's true area is not a whole number of pixels. No step measured polygon areas or edge alignment. The analyst inferred these from equal or different counts between the two pixel rules.	yes

Numbers in the answer

The last claim check read 45 numbers in the answer. 45 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

No tool call failed.

Data integrity

Each data file has the same SHA-256 hash now as at the time of the step that read it. The run did not change the data.

**Table 3 |** Data files and their SHA-256 hashes, Opus run.
File	SHA-256	Fetched data	Steps with this hash
`{data}/rouault-gdal-autotest/byte.tif`736 bytes	`59ed6e9dd192`	the download script (fetch.sh) has no hash for this file	n1, n3, n4, n5, n6
`{data}/rouault-gdal-autotest/zones.geojson`472 bytes	`0d96b10244d0`	the download script (fetch.sh) has no hash for this file	n2, n4, n5, n6

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/rouault-gdal-autotest/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/rouault-gdal-autotest/bench.yaml.

cuvette bench papers --papers rouault-gdal-autotest --models claude:claude-opus-5-5

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

raster_info (step n1)
Run: gdalinfo -stats <in>.tif
- <in>.tif
```
{data}/rouault-gdal-autotest/byte.tif
```
- band = 1
The manual route that the harness recorded
```
gdalinfo -stats {data}/rouault-gdal-autotest/byte.tif
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.
vector_info (step n2)
Run: ogrinfo -so -al <in>
- <in>
```
{data}/rouault-gdal-autotest/zones.geojson
```
The manual route that the harness recorded
```
ogrinfo -json -so -al {data}/rouault-gdal-autotest/zones.geojson
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.
reproject_raster (step n3)
Run: gdalwarp -t_srs <crs> -r <method> [-tr <size> <size>] [-dstnodata <value>] <in>.tif <out>.tif
- <in>.tif
```
{data}/rouault-gdal-autotest/byte.tif
```
- -t_srs = EPSG:4326
- -r = near
The manual route that the harness recorded
```
gdalwarp -overwrite -of GTiff -t_srs EPSG:4326 -r near {data}/rouault-gdal-autotest/byte.tif {work}/reproject_raster-1/result.tif
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.
zonal_stats (step n4)
QGIS: Processing Toolbox>Raster analysis > Zonal statistics. Code: rasterstats.zonal_stats(zones, raster, stats=[...], all_touched=<bool>)
- Raster layer
```
{data}/rouault-gdal-autotest/byte.tif
```
- Vector layer containing zones
```
{data}/rouault-gdal-autotest/zones.geojson
```
- all_touched = centers
- Note: The rasterstats code runs here. The QGIS dialog counts a pixel by its center, which matches centers. The all_touched setting has no switch in that dialog. The route in QGIS is not tested.
The manual route that the harness recorded
```
rasterstats.zonal_stats('{data}/rouault-gdal-autotest/zones.geojson', '{data}/rouault-gdal-autotest/byte.tif', all_touched=False, nodata=None)
```
The manual route uses the same method. The note in the route gives the known difference.
plot_map (step n6)
QGIS: add the raster layer, add the polygon layer, set the polygon fill to none, then Project>Import/Export>Export Map to Image
- Raster layer
```
{data}/rouault-gdal-autotest/byte.tif
```
- Vector layer
```
{data}/rouault-gdal-autotest/zones.geojson
```
- Warning: If you keep the default , you get a different result.
- Note: The picture comes from matplotlib, not from QGIS. The colors and the layout differ. The pixel values are the same.
The manual route that the harness recorded
```
Open {data}/rouault-gdal-autotest/byte.tif in QGIS and add {data}/rouault-gdal-autotest/zones.geojson as an outline layer
```
The manual route uses the same method. The note in the route gives the known difference.

calculate (step n7)

Run the tool "calculate" with these settings: {"items":[{"name":"B_extra_pixels_all_touched","expression":"56 - 42"},{"name":"B_mean_difference","expression":"134.85714285714286 - 135.0"}]}.
- Code only: this step has no route in the program menus. Run it with the script or flow export.

The harness recorded no manual route for this step.

calculate (step n8)

Run the tool "calculate" with these settings: {"items":[{"name":"B_mean_difference_rounded","expression":"round(134.85714285714286 - 135.0, 3)"}]}.
- Code only: this step has no route in the program menus. Run it with the script or flow export.

The harness recorded no manual route for this step.

Figure

Run facts

**Table 4 |** Run facts, Opus run.
Model	claude-opus-5-5 through the Anthropic service
Date	2026-10-09 13:18:00 UTC
End of run	the model gave a final answer
Time	113 s
Requests to the model	9
Tokensunits of text that the model read and wrote	22 input, 4712 output, 128758 cache read, 20309 cache write
Cost estimate	$0.22 at list price, from the token counts
Tool calls	11 (0 failed)
Adapters	gdal 0.1.1, program 3.13.3
Session	`20261009-081759-39eb`

Code hash of each step (8)

**Table 5 |** Code hash of each step, Opus run.
Step	Tool	Program version	Code hash
n1	`raster_info`	3.13.3	`dba2f5e14921`
n2	`vector_info`	3.13.3	`27441ebbe414`
n3	`reproject_raster`	3.13.3	`86820a0f856c`
n4	`zonal_stats`	3.13.3	`3f6d6ffc18ca`
n5 comparison	`zonal_stats`	3.13.3	`3f6d6ffc18ca`
n6	`plot_map`	3.13.3	`d661c9072029`
n7	`calculate`	-	`d864d37ef90b`
n8	`calculate`	-	`d864d37ef90b`

The code hash is a fingerprint of the adapter name, the adapter version, the tool and its definition in the adapter. If one of these changes, the hash changes.

Model in this columnSonnet · claude-sonnet-5-5 · run 3 of 3 shown 14 of 14 values match, 14 of 14 correct in the final answer

The session

setup The decision record starts with these values. A script set them before the first message, from the answers that we wrote before the run.

Target coordinate reference system (CRS): EPSG:4326Source in the tutorial or test suite: The request asks for latitude and longitude. The test suite has no warp step for this file, so we chose this CRS.
Resampling method: nearSource in the tutorial or test suite: Not in a source. We use the gdalwarp default. The warp is only a size check.
Nodata value: Source in the tutorial or test suite: The file has no nodata value. We set none.
Output pixel size: Source in the tutorial or test suite: Not in a source. We use the gdalwarp default.
Pixels that a zone edge cuts: centersSource in the tutorial or test suite: The request states this rule for the second square. It is also the default in rasterstats.

harness note A note from the harness to the model. The scientist did not write it.

- Setup values:
Geometry:
- Target coordinate reference system (target_crs): EPSG:4326
- Resampling method (resampling): near
- Output pixel size (pixel_size): 
Data:
- Nodata value (nodata): 
Zonal statistics:
- Pixels that a zone edge cuts (partial_pixels): centers
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

The model reads the method notes describe-and-reproject.

The model reads the method notes zonal-statistics.

Model

Setup values are already set. I start by reading both files.

The model calls raster_info (adapter gdal).

step n1 raster_info adapter gdal 0.1.1, GDAL 3.13.3

20 x 20 pixels, EPSG:26711, mean 126.765, stddev 22.928.

Input file: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192.

Arguments

path	`{data}/rouault-gdal-autotest/byte.tif`

Tool output

{
 "ok": true,
 "metrics": {
  "width": 20,
  "height": 20,
  "n_bands": 1,
  "pixel_x": 60,
  "pixel_y": 60,
  "origin_x": 440720,
  "origin_y": 3751320,
  "epsg": 26711,
  "min": 74,
  "max": 255,
  "mean": 126.765,
  "stddev": 22.928
 },
 "data": {
  "crs_name": "NAD27 / UTM zone 11N",
  "driver": "GTiff",
  "type": "Byte",
  "corner_ul": [
   440720,
   3751320
  ],
  "corner_lr": [
   441920,
   3750120
  ]
 },
 "manualRoute": "gdalinfo -stats {data}/rouault-gdal-autotest/byte.tif"
}

The model calls vector_info (adapter gdal).

step n2 vector_info adapter gdal 0.1.1, GDAL 3.13.3

2 features, EPSG:26711.

Input file: {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Arguments

path	`{data}/rouault-gdal-autotest/zones.geojson`

Tool output

{
 "ok": true,
 "metrics": {
  "n_features": 2,
  "n_layers": 1,
  "n_fields": 1,
  "epsg": 26711,
  "xmin": 440720,
  "ymin": 3750720,
  "xmax": 441320,
  "ymax": 3751320
 },
 "data": {
  "layer": "zones",
  "geometry_type": "Polygon",
  "fields": [
   "id"
  ]
 },
 "manualRoute": "ogrinfo -json -so -al {data}/rouault-gdal-autotest/zones.geojson"
}

Model thinking, summary: No nodata value was specified, so I'll proceed with reprojecting and running zonal statistics directly on the original raster, keeping its existing CRS.

Model

The raster and the squares share EPSG:26711. Next I reproject the raster to EPSG:4326 with nearest neighbour, and run zonal statistics on the original raster.

The model calls reproject_raster (adapter gdal).

step n3 reproject_raster adapter gdal 0.1.1, GDAL 3.13.3

Reproject a raster finished. width=22, height=18, n_bands=1, pixel_x=0.00059812.

Decisions applied: Target coordinate reference system (CRS) = EPSG:4326; Resampling method = near.

Input file: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192.

Outputs: result (4e4c38b9dbb7).

Arguments

path	`{data}/rouault-gdal-autotest/byte.tif`
target_crs	`EPSG:4326`
resampling	`near`

Tool output

{
 "ok": true,
 "metrics": {
  "width": 22,
  "height": 18,
  "n_bands": 1,
  "pixel_x": 0.0005981204678461,
  "pixel_y": 0.0005981204678461,
  "origin_x": -117.64205396140792,
  "origin_y": 33.902434573500166,
  "epsg": 4326,
  "min": 74,
  "max": 255,
  "mean": 126.551,
  "stddev": 21.882,
  "src_width": 20,
  "src_height": 20
 },
 "data": {
  "crs_name": "WGS 84",
  "driver": "GTiff",
  "type": "Byte",
  "corner_ul": [
   -117.642054,
   33.9024346
  ],
  "corner_lr": [
   -117.6288953,
   33.8916684
  ]
 },
 "manualRoute": "gdalwarp -overwrite -of GTiff -t_srs EPSG:4326 -r near {data}/rouault-gdal-autotest/byte.tif {work}/reproject_raster-1/result.tif"
}

The model calls zonal_stats (adapter gdal).

step n4 zonal_stats adapter gdal 0.1.1, GDAL 3.13.3

2 zone(s). First zone: 100 pixels, mean 129.5.

Decisions applied: Pixels that a zone edge cuts = centers.

Input files: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192; {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Outputs: zonal_stats.csv (a0eef9b5a536).

Arguments

raster	`{data}/rouault-gdal-autotest/byte.tif`
zones	`{data}/rouault-gdal-autotest/zones.geojson`
id_field	`id`
pixels	`centers`

Tool output

{
 "ok": true,
 "metrics": {
  "n_zones": 2,
  "count": 100,
  "min": 99,
  "max": 197,
  "mean": 129.5,
  "sum": 12950,
  "std": 17.90335164152232,
  "median": 132
 },
 "data": {
  "zones": [
   {
    "zone": "A",
    "count": 100,
    "min": 99,
    "max": 197,
    "mean": 129.5,
    "sum": 12950,
    "std": 17.90335164152232,
    "median": 132
   },
   {
    "zone": "B",
    "count": 42,
    "min": 99,
    "max": 173,
    "mean": 135,
    "sum": 5670,
    "std": 17.210185245675778,
    "median": 132
   }
  ],
  "pixels": "centers",
  "nodata": null,
  "raster_epsg": 26711
 },
 "manualRoute": "rasterstats.zonal_stats('{data}/rouault-gdal-autotest/zones.geojson', '{data}/rouault-gdal-autotest/byte.tif', all_touched=False, nodata=None)",
 "table": {
  "columns": [
   "zone",
   "count",
   "min",
   "max",
   "mean",
   "sum",
   "std",
   "median"
  ],
  "rows": [
   [
    "A",
    100,
    99,
    197,
    129.5,
    12950,
    17.90335164152232,
    132
   ],
   [
    "B",
    42,
    99,
    173,
    135,
    5670,
    17.210185245675778,
    132
   ]
  ],
  "n_rows": 2,
  "path": "{work}/zonal_stats-1/zonal_stats.csv"
 },
 "outputs": [
  {
   "path": "{work}/zonal_stats-1/zonal_stats.csv",
   "kind": "table",
   "name": "zonal_stats.csv"
  }
 ]
}

The model calls zonal_stats (adapter gdal).

deviation The model asked for pixels = all_touched. The scientist chose centers for Pixels that a zone edge cuts. The harness kept centers.

step n5 zonal_stats adapter gdal 0.1.1, GDAL 3.13.3

2 zone(s). First zone: 100 pixels, mean 129.5.

Decisions applied: Pixels that a zone edge cuts = centers.

Input files: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192; {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Outputs: zonal_stats.csv (a0eef9b5a536).

Arguments

raster	`{data}/rouault-gdal-autotest/byte.tif`
zones	`{data}/rouault-gdal-autotest/zones.geojson`
id_field	`id`
pixels	`centers`

Tool output

{
 "ok": true,
 "metrics": {
  "n_zones": 2,
  "count": 100,
  "min": 99,
  "max": 197,
  "mean": 129.5,
  "sum": 12950,
  "std": 17.90335164152232,
  "median": 132
 },
 "data": {
  "zones": [
   {
    "zone": "A",
    "count": 100,
    "min": 99,
    "max": 197,
    "mean": 129.5,
    "sum": 12950,
    "std": 17.90335164152232,
    "median": 132
   },
   {
    "zone": "B",
    "count": 42,
    "min": 99,
    "max": 173,
    "mean": 135,
    "sum": 5670,
    "std": 17.210185245675778,
    "median": 132
   }
  ],
  "pixels": "centers",
  "nodata": null,
  "raster_epsg": 26711
 },
 "manualRoute": "rasterstats.zonal_stats('{data}/rouault-gdal-autotest/zones.geojson', '{data}/rouault-gdal-autotest/byte.tif', all_touched=False, nodata=None)",
 "table": {
  "columns": [
   "zone",
   "count",
   "min",
   "max",
   "mean",
   "sum",
   "std",
   "median"
  ],
  "rows": [
   [
    "A",
    100,
    99,
    197,
    129.5,
    12950,
    17.90335164152232,
    132
   ],
   [
    "B",
    42,
    99,
    173,
    135,
    5670,
    17.210185245675778,
    132
   ]
  ],
  "n_rows": 2,
  "path": "{work}/zonal_stats-2/zonal_stats.csv"
 },
 "outputs": [
  {
   "path": "{work}/zonal_stats-2/zonal_stats.csv",
   "kind": "table",
   "name": "zonal_stats.csv"
  }
 ]
}

Model thinking, summary: The all_touched override gave the same results, with zones A and B identified by the id field. I'll quickly generate a map plot to verify this visually.

The model calls plot_map (adapter gdal).

step n6 plot_map adapter gdal 0.1.1, GDAL 3.13.3

Map of 400 valid pixels.

Input files: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192; {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Outputs: result (805e1816caf2).

Arguments

raster	`{data}/rouault-gdal-autotest/byte.tif`
zones	`{data}/rouault-gdal-autotest/zones.geojson`
title	`byte.tif with zones A and B (EPSG:26711)`

Tool output

{
 "ok": true,
 "metrics": {
  "width_px": 20,
  "height_px": 20,
  "valid_pixels": 400
 },
 "data": {
  "colormap": "viridis"
 },
 "manualRoute": "Open {data}/rouault-gdal-autotest/byte.tif in QGIS and add {data}/rouault-gdal-autotest/zones.geojson as an outline layer",
 "outputs": [
  {
   "path": "{work}/plot_map-1/result.png",
   "kind": "image",
   "name": "map.png"
  }
 ]
}

Model Answer draft 1. The claim check and the review read it.

What I did

I read the raster and the squares with raster_info and vector_info.
I reprojected the raster to EPSG:4326 with nearest neighbour (reproject_raster). I left pixel size and nodata empty, so GDAL chose them.
I ran zonal statistics on the original raster with zonal_stats, counting pixel centers. I plotted the squares on the raster with plot_map.

The raster and the squares both use EPSG:26711, so I did not reproject the squares.

Findings

Size, projection and pixel size (raster_info)
The image is 20 pixels wide and 20 pixels high, with 1 band.
It uses EPSG:26711 (NAD27 / UTM zone 11N).
One pixel is 60 m × 60 m on the ground.

Pixel statistics (raster_info)
The lowest value is 74 and the highest is 255.
The mean is 126.765 and the standard deviation is 22.928.
The file has no nodata value, so no pixels were skipped. The plot shows 400 valid pixels.

Image in latitude and longitude (reproject_raster)
The new image is 22 pixels wide and 18 pixels high.
It uses EPSG:4326 with nearest-neighbour resampling.
One pixel is 0.00059812 degrees. This is a size in degrees, not metres.
The statistics change a little after resampling. The mean is 126.551 and the standard deviation is 21.882. The minimum and maximum stay at 74 and 255.

First square, zone A (zonal_stats, pixel centers, nodata not set)
It covers 100 pixels.
The mean is 129.5. The minimum is 99, the maximum is 197 and the sum is 12950.
This square follows pixel edges. It has the same extent as the one in the map, 440720 to 441320 m east and 3750720 to 3751320 m north.

Second square, zone B (zonal_stats, pixel centers, nodata not set)
It covers 42 pixels.
The mean is 135.0. The minimum is 99, the maximum is 173 and the sum is 5670.
The polygon names come from the id field. The tool read them as A and B. I took A as "first" because it is the first feature.

What is uncertain

My all_touched request was overridden by the recorded centers rule. I have no all-touched counts. Zone B would probably gain pixels under that rule, because the square cuts pixels. I can run compare_options to check.
The zone A and zone B means used the same rule (pixel centers), so you can compare them.
Zone B has few pixels, so its mean depends on which edge pixels fall inside.
The 22 × 18 size depends on the pixel size that GDAL chose. A different pixel size gives a different size.
Nodata is empty. If you want a nodata value, for example 0 or 255, tell me.

What waits for you

Do you want an all_touched comparison for zone B?
Do you want a set pixel size or nodata value for the reprojection?

claim check 36 numbers in the answer, 0 numbers without a source in the record.

The review found 1 problem. The model corrects the answer before the scientist sees it.

review The review sent the answer back to the model before the scientist saw it.

- The answer says the file has no nodata value and no pixels were skipped. No step checked the nodata value. The raster_info result gives no nodata field, and the plot only counted 400 valid pixels.
Fix these problems. Then give the answer again.

Model thinking, summary: I notice the raster_info metrics don't include a nodata field, so I should try running a script to check nodata via gdalinfo or rasterio directly.

Model

I will check the nodata value in the file header before I repeat the answer.

The model runs run_script.

import subprocess
r = subprocess.run(["gdalinfo","{data}/rouault-gdal-autotest/byte.tif"],capture_output=True,text=True)
print(r.stdout[-1500:], r.stderr[-500:])

The harness lets the model use run_script. In this validation, the harness lets the model use every tool and does not ask the scientist.

Result of run_script: The script ran in {work} and wrote 0 new file(s) to {work}.

TER["Scale factor at natural origin",0.9996,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["North America - between 120°W and 114°W - onshore. Canada - Alberta; British Columbia; Northwest Territories; Nunavut. Mexico. United States (USA) - California; Idaho; Nevada; Oregon; Washington."],
        BBOX[26.93,-120,78.13,-114]],
    ID["EPSG",26711]]
Data axis to CRS axis mapping: 1,2
Origin = (440720.000000000000000,3751320.000000000000000)
Pixel Size = (60.000000000000000,-60.000000000000000)
Metadata:
  AREA_OR_POINT=Area
Image Structure Metadata:
  INTERLEAVE=BAND
Corner Coordinates:
Upper Left  (  440720.000, 3751320.000) (117d38'28.21"W, 33d54' 8.47"N)
Lower Left  (  440720.000, 3750120.000) (117d38'27.92"W, 33d53'29.51"N)
Upper Right (  441920.000, 3751320.000) (117d37'41.48"W, 33d54' 8.71"N)
Lower Right (  441920.000, 3750120.000) (117d37'41.20"W, 33d53'29.75"N)
Center      (  441320.000, 3750720.000) (117d38' 4.70"W, 33d53'49.11"N)
Band 1 Block=20x20 Type=Byte, ColorInterp=Gray

Model The model writes its answer. The claim check reads it. See the final answer.

claim check 32 numbers in the answer, 0 numbers without a source in the record.

Saved: results.xlsx (7 sheets), 2 figures in figures/. Folder: {work}

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

The header of byte.tif has no "NoData Value" line. The band line shows only Type=Byte and ColorInterp=Gray. I checked this with gdalinfo.

Corrected answer

What I did

I read the raster and the squares with raster_info and vector_info.
I reprojected the raster to EPSG:4326 with nearest neighbour (reproject_raster). I left pixel size and nodata empty, so GDAL chose them.
I ran zonal statistics on the original raster with zonal_stats, counting pixel centers. I plotted the squares with plot_map.
I read the file header with gdalinfo to check the nodata value.

The raster and the squares both use EPSG:26711, so I did not reproject the squares.

Findings

Size, projection and pixel size (raster_info)
The image is 20 pixels wide and 20 pixels high, with 1 band.
It uses EPSG:26711 (NAD27 / UTM zone 11N).
One pixel is 60 m × 60 m on the ground.

Pixel statistics (raster_info)
The lowest value is 74 and the highest is 255.
The mean is 126.765 and the standard deviation is 22.928.
Nodata: the file header has no nodata value (gdalinfo). The statistics therefore use all 400 pixels, and plot_map also counted 400 valid pixels. The value 255 is a real pixel value here.

Image in latitude and longitude (reproject_raster)
The new image is 22 pixels wide and 18 pixels high.
It uses EPSG:4326 with nearest-neighbour resampling.
One pixel is 0.00059812 degrees. This is a size in degrees, not metres.
The statistics change a little after resampling. The mean is 126.551 and the standard deviation is 21.882. The minimum and maximum stay at 74 and 255.

First square, zone A (zonal_stats, pixel centers, no nodata)
It covers 100 pixels.
The mean is 129.5. The minimum is 99, the maximum is 197 and the sum is 12950.

Second square, zone B (zonal_stats, pixel centers, no nodata)
It covers 42 pixels.
The mean is 135.0. The minimum is 99, the maximum is 173 and the sum is 5670.
The polygon names come from the id field. The tool read them as A and B. I took A as "first" because it is the first feature.

What is uncertain

My all_touched request was overridden by the recorded centers rule. I have no all-touched counts for zone B.
Zone B has few pixels, so its mean depends on which edge pixels fall inside.
The 22 × 18 size depends on the pixel size that GDAL chose. A different pixel size gives a different size.
Zone A and zone B used the same pixel rule, so you can compare their means.

What waits for you

Do you want an all_touched comparison for zone B?
Do you want a set pixel size for the reprojection?
Do you want to set a nodata value? The file has none, so a value such as 255 would skip real pixels.

Settings and step results that the answer does not mention. The harness adds them.

Settings used, from the decision record: Target coordinate reference system: EPSG:4326 · Resampling method: near · Pixels that a zone edge cuts: centers.

Checks

Review findings

The review recorded 4 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

**Table 6 |** Review findings, Sonnet run.
Severity	From	Finding	Shown with the final answer
warning	referee model	The gdalinfo output in the log is cut off right after 'ColorInterp=Gr...'. A NoData line would come after that point. The log does not show that the header has no nodata value, yet the answer states it as fact.	yes
warning	referee model	The all_touched request in step 7 was overridden to centers. The result repeats step 6 exactly. The log has no all-touched run. The answer admits this for zone B but must say it applies to zone A too.	yes
warning	referee model	The report does not say why nearest neighbour was used for the reprojection. It must give the reason. It also does not say if the reprojected raster has a nodata fill or how many pixels the statistics skipped. The mean changed from 126.765 to 126.551 on a 22 x 18 grid. The log does not show whether edge fill or resampling caused the change.	yes
info	referee model	The report names the CRS 'NAD27 / UTM zone 11N', but the visible log shows only the code EPSG:26711. The name is probably correct, but the log does not show it. The claim that zone B's mean 'depends on which edge pixels fall inside' is a guess that no step tested.	yes

Numbers in the answer

The last claim check read 32 numbers in the answer. 32 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model asked for pixels = all_touched. The scientist chose centers for Pixels that a zone edge cuts. The harness kept centers.

Failed tool calls

No tool call failed.

Data integrity

Each data file has the same SHA-256 hash now as at the time of the step that read it. The run did not change the data.

**Table 7 |** Data files and their SHA-256 hashes, Sonnet run.
File	SHA-256	Fetched data	Steps with this hash
`{data}/rouault-gdal-autotest/byte.tif`736 bytes	`59ed6e9dd192`	the download script (fetch.sh) has no hash for this file	n1, n3, n4, n5, n6
`{data}/rouault-gdal-autotest/zones.geojson`472 bytes	`0d96b10244d0`	the download script (fetch.sh) has no hash for this file	n2, n4, n5, n6

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/rouault-gdal-autotest/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/rouault-gdal-autotest/bench.yaml.

cuvette bench papers --papers rouault-gdal-autotest --models claude:claude-sonnet-5-5

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

raster_info (step n1)
Run: gdalinfo -stats <in>.tif
- <in>.tif
```
{data}/rouault-gdal-autotest/byte.tif
```
The manual route that the harness recorded
```
gdalinfo -stats {data}/rouault-gdal-autotest/byte.tif
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.
vector_info (step n2)
Run: ogrinfo -so -al <in>
- <in>
```
{data}/rouault-gdal-autotest/zones.geojson
```
The manual route that the harness recorded
```
ogrinfo -json -so -al {data}/rouault-gdal-autotest/zones.geojson
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.
reproject_raster (step n3)
Run: gdalwarp -t_srs <crs> -r <method> [-tr <size> <size>] [-dstnodata <value>] <in>.tif <out>.tif
- <in>.tif
```
{data}/rouault-gdal-autotest/byte.tif
```
- -t_srs = EPSG:4326
- -r = near
The manual route that the harness recorded
```
gdalwarp -overwrite -of GTiff -t_srs EPSG:4326 -r near {data}/rouault-gdal-autotest/byte.tif {work}/reproject_raster-1/result.tif
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.
zonal_stats (step n4)
QGIS: Processing Toolbox>Raster analysis > Zonal statistics. Code: rasterstats.zonal_stats(zones, raster, stats=[...], all_touched=<bool>)
- Raster layer
```
{data}/rouault-gdal-autotest/byte.tif
```
- Vector layer containing zones
```
{data}/rouault-gdal-autotest/zones.geojson
```
- all_touched = centers
- Note: The rasterstats code runs here. The QGIS dialog counts a pixel by its center, which matches centers. The all_touched setting has no switch in that dialog. The route in QGIS is not tested.
The manual route that the harness recorded
```
rasterstats.zonal_stats('{data}/rouault-gdal-autotest/zones.geojson', '{data}/rouault-gdal-autotest/byte.tif', all_touched=False, nodata=None)
```
The manual route uses the same method. The note in the route gives the known difference.
zonal_stats (step n5)
QGIS: Processing Toolbox>Raster analysis > Zonal statistics. Code: rasterstats.zonal_stats(zones, raster, stats=[...], all_touched=<bool>)
- Raster layer
```
{data}/rouault-gdal-autotest/byte.tif
```
- Vector layer containing zones
```
{data}/rouault-gdal-autotest/zones.geojson
```
- all_touched = centers
- Note: The rasterstats code runs here. The QGIS dialog counts a pixel by its center, which matches centers. The all_touched setting has no switch in that dialog. The route in QGIS is not tested.
The manual route that the harness recorded
```
rasterstats.zonal_stats('{data}/rouault-gdal-autotest/zones.geojson', '{data}/rouault-gdal-autotest/byte.tif', all_touched=False, nodata=None)
```
The manual route uses the same method. The note in the route gives the known difference.
plot_map (step n6)
QGIS: add the raster layer, add the polygon layer, set the polygon fill to none, then Project>Import/Export>Export Map to Image
- Raster layer
```
{data}/rouault-gdal-autotest/byte.tif
```
- Vector layer
```
{data}/rouault-gdal-autotest/zones.geojson
```
- Warning: If you keep the default , you get a different result.
- Note: The picture comes from matplotlib, not from QGIS. The colors and the layout differ. The pixel values are the same.
The manual route that the harness recorded
```
Open {data}/rouault-gdal-autotest/byte.tif in QGIS and add {data}/rouault-gdal-autotest/zones.geojson as an outline layer
```
The manual route uses the same method. The note in the route gives the known difference.

Figure

Run facts

**Table 8 |** Run facts, Sonnet run.
Model	claude-sonnet-5-5 through the Anthropic service
Date	2026-10-09 13:06:21 UTC
End of run	the model gave a final answer
Time	101 s
Requests to the model	7
Tokensunits of text that the model read and wrote	18 input, 3972 output, 91155 cache read, 22353 cache write
Cost estimate	$0.11 at list price, from the token counts
Tool calls	9 (0 failed)
Adapters	gdal 0.1.1, program 3.13.3
Session	`20261009-080617-a4c2`

Code hash of each step (6)

**Table 9 |** Code hash of each step, Sonnet run.
Step	Tool	Program version	Code hash
n1	`raster_info`	3.13.3	`dba2f5e14921`
n2	`vector_info`	3.13.3	`27441ebbe414`
n3	`reproject_raster`	3.13.3	`86820a0f856c`
n4	`zonal_stats`	3.13.3	`3f6d6ffc18ca`
n5	`zonal_stats`	3.13.3	`3f6d6ffc18ca`
n6	`plot_map`	3.13.3	`d661c9072029`

The code hash is a fingerprint of the adapter name, the adapter version, the tool and its definition in the adapter. If one of these changes, the hash changes.

Model in this columnHaiku · claude-haiku-5-5 · run 3 of 3 shown 14 of 14 values match, 14 of 14 correct in the final answer

The session

setup The decision record starts with these values. A script set them before the first message, from the answers that we wrote before the run.

Target coordinate reference system (CRS): EPSG:4326Source in the tutorial or test suite: The request asks for latitude and longitude. The test suite has no warp step for this file, so we chose this CRS.
Resampling method: nearSource in the tutorial or test suite: Not in a source. We use the gdalwarp default. The warp is only a size check.
Nodata value: Source in the tutorial or test suite: The file has no nodata value. We set none.
Output pixel size: Source in the tutorial or test suite: Not in a source. We use the gdalwarp default.
Pixels that a zone edge cuts: centersSource in the tutorial or test suite: The request states this rule for the second square. It is also the default in rasterstats.

harness note A note from the harness to the model. The scientist did not write it.

- Setup values:
Geometry:
- Target coordinate reference system (target_crs): EPSG:4326
- Resampling method (resampling): near
- Output pixel size (pixel_size): 
Data:
- Nodata value (nodata): 
Zonal statistics:
- Pixels that a zone edge cuts (partial_pixels): centers
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

Model thinking, summary: I'll start by gathering info on the raster and vector files, then plan to pull stats from the raster, reproject it to EPSG:4326, and run zonal statistics using the id field with pixel centers on the original raster before finalizing an answer.

Model

I will read the raster and the zone file first. This shows the size, projection and pixel size that questions 1 and 2 need.

The model calls raster_info (adapter gdal).

step n1 raster_info adapter gdal 0.1.1, GDAL 3.13.3

20 x 20 pixels, EPSG:26711, mean 126.765, stddev 22.928.

Input file: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192.

Arguments

path	`{data}/rouault-gdal-autotest/byte.tif`

Tool output

{
 "ok": true,
 "metrics": {
  "width": 20,
  "height": 20,
  "n_bands": 1,
  "pixel_x": 60,
  "pixel_y": 60,
  "origin_x": 440720,
  "origin_y": 3751320,
  "epsg": 26711,
  "min": 74,
  "max": 255,
  "mean": 126.765,
  "stddev": 22.928
 },
 "data": {
  "crs_name": "NAD27 / UTM zone 11N",
  "driver": "GTiff",
  "type": "Byte",
  "corner_ul": [
   440720,
   3751320
  ],
  "corner_lr": [
   441920,
   3750120
  ]
 },
 "manualRoute": "gdalinfo -stats {data}/rouault-gdal-autotest/byte.tif"
}

The model calls vector_info (adapter gdal).

step n2 vector_info adapter gdal 0.1.1, GDAL 3.13.3

2 features, EPSG:26711.

Input file: {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Arguments

path	`{data}/rouault-gdal-autotest/zones.geojson`

Tool output

{
 "ok": true,
 "metrics": {
  "n_features": 2,
  "n_layers": 1,
  "n_fields": 1,
  "epsg": 26711,
  "xmin": 440720,
  "ymin": 3750720,
  "xmax": 441320,
  "ymax": 3751320
 },
 "data": {
  "layer": "zones",
  "geometry_type": "Polygon",
  "fields": [
   "id"
  ]
 },
 "manualRoute": "ogrinfo -json -so -al {data}/rouault-gdal-autotest/zones.geojson"
}

Model

The image is 20 by 20 pixels in EPSG:26711 with 60 m pixels. The zones use the same CRS, so zonal statistics can run on the original image. Next I reproject the image to latitude and longitude, and I count the pixels in each zone at the same time.

The model calls reproject_raster (adapter gdal).

step n3 reproject_raster adapter gdal 0.1.1, GDAL 3.13.3

Reproject a raster finished. width=22, height=18, n_bands=1, pixel_x=0.00059812.

Decisions applied: Target coordinate reference system (CRS) = EPSG:4326; Resampling method = near.

Input file: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192.

Outputs: result (4e4c38b9dbb7).

Arguments

path	`{data}/rouault-gdal-autotest/byte.tif`
target_crs	`EPSG:4326`
resampling	`near`

Tool output

{
 "ok": true,
 "metrics": {
  "width": 22,
  "height": 18,
  "n_bands": 1,
  "pixel_x": 0.0005981204678461,
  "pixel_y": 0.0005981204678461,
  "origin_x": -117.64205396140792,
  "origin_y": 33.902434573500166,
  "epsg": 4326,
  "min": 74,
  "max": 255,
  "mean": 126.551,
  "stddev": 21.882,
  "src_width": 20,
  "src_height": 20
 },
 "data": {
  "crs_name": "WGS 84",
  "driver": "GTiff",
  "type": "Byte",
  "corner_ul": [
   -117.642054,
   33.9024346
  ],
  "corner_lr": [
   -117.6288953,
   33.8916684
  ]
 },
 "manualRoute": "gdalwarp -overwrite -of GTiff -t_srs EPSG:4326 -r near {data}/rouault-gdal-autotest/byte.tif {work}/reproject_raster-1/result.tif"
}

The model calls zonal_stats (adapter gdal).

step n4 zonal_stats adapter gdal 0.1.1, GDAL 3.13.3

2 zone(s). First zone: 100 pixels, mean 129.5.

Decisions applied: Pixels that a zone edge cuts = centers.

Input files: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192; {data}/rouault-gdal-autotest/zones.geojson SHA-256 0d96b10244d0.

Outputs: zonal_stats.csv (a0eef9b5a536).

Arguments

raster	`{data}/rouault-gdal-autotest/byte.tif`
zones	`{data}/rouault-gdal-autotest/zones.geojson`
id_field	`id`
pixels	`centers`

Tool output

{
 "ok": true,
 "metrics": {
  "n_zones": 2,
  "count": 100,
  "min": 99,
  "max": 197,
  "mean": 129.5,
  "sum": 12950,
  "std": 17.90335164152232,
  "median": 132
 },
 "data": {
  "zones": [
   {
    "zone": "A",
    "count": 100,
    "min": 99,
    "max": 197,
    "mean": 129.5,
    "sum": 12950,
    "std": 17.90335164152232,
    "median": 132
   },
   {
    "zone": "B",
    "count": 42,
    "min": 99,
    "max": 173,
    "mean": 135,
    "sum": 5670,
    "std": 17.210185245675778,
    "median": 132
   }
  ],
  "pixels": "centers",
  "nodata": null,
  "raster_epsg": 26711
 },
 "manualRoute": "rasterstats.zonal_stats('{data}/rouault-gdal-autotest/zones.geojson', '{data}/rouault-gdal-autotest/byte.tif', all_touched=False, nodata=None)",
 "table": {
  "columns": [
   "zone",
   "count",
   "min",
   "max",
   "mean",
   "sum",
   "std",
   "median"
  ],
  "rows": [
   [
    "A",
    100,
    99,
    197,
    129.5,
    12950,
    17.90335164152232,
    132
   ],
   [
    "B",
    42,
    99,
    173,
    135,
    5670,
    17.210185245675778,
    132
   ]
  ],
  "n_rows": 2,
  "path": "{work}/zonal_stats-1/zonal_stats.csv"
 },
 "outputs": [
  {
   "path": "{work}/zonal_stats-1/zonal_stats.csv",
   "kind": "table",
   "name": "zonal_stats.csv"
  }
 ]
}

Model

Here are the results for your five questions. The settings came from the harness: target CRS EPSG:4326, nearest-neighbour resampling, no set nodata value, and pixel-center counting.

1. Size, projection and pixel size (tool: raster_info)

The image is 20 pixels wide and 20 pixels high (400 pixels).
The projection is EPSG:26711, NAD27 / UTM zone 11N (North American Datum 1927, Universal Transverse Mercator zone 11 North).
One pixel is 60 m by 60 m.

2. Pixel value statistics (tool: raster_info, which runs gdalinfo with stats)

Minimum: 74
Maximum: 255
Mean: 126.765
Standard deviation: 22.928
The file has no nodata value, so all 400 pixels count.

3. Latitude and longitude image (tool: reproject_raster)

The new image is 22 pixels wide and 18 pixels high.
The projection is EPSG:4326, WGS 84. The pixel size is 0.00059812 degrees. This is an angle, not metres, so I do not give a ground distance.
The resampling method is near (nearest neighbour), so pixel values are copied, not blended.
The new image has no nodata value set. Its statistics change because of the resampling: mean 126.551 and standard deviation 21.882, against 126.765 and 22.928 in the original. Do not compare these with the original statistics as if they were the same pixels.

4. First square (zone A) (tool: zonal_stats, on the original image, pixel-center rule)

Pixels covered: 100.
Mean pixel value: 129.5.
Minimum 99, maximum 197, median 132.
The zones are named by the id field. The tool labels them A and B. I assume the first square you drew is A.

5. Second square (zone B), pixel-center rule (tool: zonal_stats, on the original image)

Pixels whose center is inside the square: 42.
Mean pixel value: 135.0.
Minimum 99, maximum 173, median 132.
Zone A and zone B have different pixel counts and means. The tool used the same pixel rule for both, but the two squares do not cover the same pixels.

What I measured and what I inferred

All values in questions 1 to 5 come from tool results in this session.
The pixel-center rule counts only pixels whose center is inside a square. Pixels that the edge cuts, but whose center is outside, are not counted. Counting every touched pixel would give a larger count and a different mean. I did not run that comparison.

What waits for you

Nothing waits. If you want the all-touched count for zone B, tell me and I will run it on the same image.
Confirm that the first square you drew is zone A.

harness note A note from the harness to the model. The scientist did not write it.

- You said what you will do next, but you did not call a tool. Do it now with a tool call, or give your final answer.

Model

I will read the zone file. This shows which square is first, so I can check my assumption that zone A is the first square.

The model calls read_file.

Result of read_file: Read 472 of 472 bytes from {data}/rouault-gdal-autotest/zones.geojson.

{
 "type": "FeatureCollection",
 "crs": {
  "type": "name",
  "properties": {
   "name": "urn:ogc:def:crs:EPSG::26711"
  }
 },
 "features": [
  {
   "type": "Feature",
   "properties": {
    "id": "A"
   },
   "geometry": {
    "type": "Polygon",
    "coordinates": [
     [
      [
       440720,
       3751320
      ],
      [
       441320,
       3751320
      ],
      [
       441320,
       3750720
      ],
      [
       440720,
       3750720
      ],
      [
       440720,
       3751320
      ]
     ]
    ]
   }
  },
  {
   "type": "Feature",
   "properties": {
    "id": "B"
   },
   "geometry": {
    "type": "Polygon",
    "coordinates": [
     [
      [
       440760,
       3751260
      ],
      [
       441160,
       3751260
      ],
      [
       441160,
       3750860
      ],
      [
       440760,
       3750860
      ],
      [
       440760,
       3751260
      ]
     ]
    ]
   }
  }
 ]
}

Model thinking, summary: Confirmed: feature A is a 600m square aligned to the grid, and feature B is a 400m square offset by 40m, so it doesn't align to the 60m grid and will have partial pixels. That's consistent, so I'm ready to give the final concise answer.

Model Answer draft 1. The claim check and the review read it.

The zone file confirms that zone A is the first square in the file. Its name in the id field is "A". The second square is named "B". Both squares use EPSG:26711, the same CRS as the image.

All five answers come from tool results in this session. Nothing is pending.

1. Size, projection and pixel size (raster_info)

20 pixels wide and 20 pixels high (400 pixels).
EPSG:26711, NAD27 / UTM zone 11N.
One pixel is 60 m by 60 m.

2. Pixel value statistics (raster_info)

Minimum 74, maximum 255, mean 126.765, standard deviation 22.928.
No nodata value is set in the file, so all 400 pixels count.

3. Latitude and longitude image (reproject_raster, near resampling)

22 pixels wide and 18 pixels high.
EPSG:4326, WGS 84. The pixel size is 0.00059812 degrees. This is an angle, not metres.
No nodata value is set. Resampling changed the statistics: mean 126.551 and standard deviation 21.882.

4. Zone A, the first square (zonal_stats, pixel-center rule)

100 pixels.
Mean 129.5.

5. Zone B, the second square (zonal_stats, pixel-center rule)

42 pixels whose center is inside the square.
Mean 135.0.
Zone B is 400 m wide and starts 40 m inside the grid. Its edge cuts pixels, so the pixel-center rule leaves out the edge pixels.

What is uncertain

The pixel-center rule gives a count that differs from an all-touched count. I did not run the all-touched count.

What waits for you

Nothing waits. Ask me if you want the all-touched count for zone B.

claim check 23 numbers in the answer, 4 numbers without a source in the record.

The review found 3 problems. The model corrects the answer before the scientist sees it.

review The review sent the answer back to the model before the scientist saw it.

- 4 numbers in the answer match no logged tool result: 400, 40. Run the tool that measures each number, or remove the number, or say how you derived it from logged results.
- The claim that the zone file confirms zone A is first is not supported. The read_file result shows only the byte count, not the file content. Use the zonal table names in entry 25 instead.
- The width of zone B (400 m), its 40 m offset from the grid, and the claim that its edge cuts pixels have no logged source. vector_info reports only the extent of the whole file, not the extent of zone B.
Fix these problems. Then give the answer again.

Model

I will derive the pixel total from the logged image width and height, so that it has a logged source.

The model runs calculate.

{"items":[{"name":"image_pixels","expression":"20*20"}],"purpose":"Derive the total pixel count of the original image from its logged width and height."}

step n5 calculate

Calculated 1 value.

Arguments

items [{"name":"image_pixels","expression":"20*20"}]

Tool output

image_pixels = 20*20 = 400

Model thinking, summary: I should drop the zone B width/offset claims since they're unreliable, and pull zone A's name from the zonal stats table rows instead. I'll briefly flag the disagreement with the read_file output but keep the rest consistent.

Model

The corrected answers follow. The first two review points are fixed. The zone B width, the 40 m offset and the edge-cut claim are removed, because no logged tool result gives them.

1. Size, projection and pixel size (raster_info)

20 pixels wide and 20 pixels high. The total is 400 pixels (calculate: 20 × 20 = 400).
EPSG:26711, NAD27 / UTM zone 11N (North American Datum 1927, Universal Transverse Mercator zone 11 North).
One pixel is 60 m by 60 m.

2. Pixel value statistics (raster_info, which runs gdalinfo with stats)

Minimum 74, maximum 255, mean 126.765, standard deviation 22.928.
No nodata value is set in the file, so all 400 pixels count.

3. Latitude and longitude image (reproject_raster, near resampling)

22 pixels wide and 18 pixels high.
EPSG:4326, WGS 84. The pixel size is 0.00059812 degrees. This is an angle, not metres, so I give no ground distance.
No nodata value is set. Resampling changed the statistics: mean 126.551 and standard deviation 21.882, against 126.765 and 22.928 in the original.

4. Zone A, the first square (zonal_stats, pixel-center rule, original image)

100 pixels.
Mean 129.5.
The zonal table names this zone A. I assume it is the first square you drew. I did not confirm this from the file.

5. Zone B, the second square (zonal_stats, pixel-center rule, original image)

42 pixels whose center is inside the square.
Mean 135.0.
The zonal table names this zone B.

What I measured and what I inferred

All values in questions 1 to 5 come from tool results in this session. The total of 400 pixels comes from the calculate result.
The pixel-center rule counts only pixels whose center is inside a square. An all-touched count would give a different number and mean. I did not run it.
The logged read_file result of the zone file does list the names A and B. I did not use that result for the answer. The names come from the zonal table only.

What waits for you

Nothing waits. Confirm that the first square you drew is zone A. If you want the all-touched count for zone B, ask me and I will run it.

harness note A note from the harness to the model. The scientist did not write it.

- You said what you will do next, but you did not call a tool. Do it now with a tool call, or give your final answer.

Model thinking, summary: Checking back, I didn't actually promise anything further—that line was conditional, not a commitment. Nothing's pending, so I should just give the final concise answer with the numbers already gathered.

Model The model writes its answer. The claim check reads it. See the final answer.

claim check 20 numbers in the answer, 0 numbers without a source in the record.

Saved: results.xlsx (6 sheets), 1 figure in figures/. Folder: {work}

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

Nothing is pending. All five answers come from tool results in this session. The total of 400 pixels comes from a calculate result.

Final answers

Size, projection and pixel size (raster_info)
20 pixels wide and 20 pixels high (400 pixels in total).
EPSG:26711, NAD27 / UTM zone 11N.
One pixel is 60 m by 60 m.

Pixel values (raster_info, gdalinfo with stats)
Minimum 74, maximum 255, mean 126.765, standard deviation 22.928.
No nodata value is set, so all 400 pixels count.

Latitude and longitude image (reproject_raster, near resampling)
22 pixels wide and 18 pixels high.
EPSG:4326, WGS 84. The pixel size is 0.00059812 degrees, which is an angle, not metres.
No nodata value is set. Resampling changed the statistics to mean 126.551 and standard deviation 21.882.

First square, zone A (zonal_stats, pixel-center rule)
100 pixels.
Mean 129.5.
I assume zone A is the first square you drew. The zonal table names it A.

Second square, zone B (zonal_stats, pixel-center rule)
42 pixels whose center is inside the square.
Mean 135.0.

Uncertain

The pixel-center rule leaves out pixels that an edge touches but whose center lies outside. An all-touched count would give a different number and mean. I did not run it.

Waits for you

Confirm that the first square you drew is zone A.
Tell me if you want the all-touched count for zone B.

Settings and step results that the answer does not mention. The harness adds them.

Settings used, from the decision record: Target coordinate reference system: EPSG:4326 · Resampling method: near · Pixels that a zone edge cuts: centers.

Checks

Review findings

The review recorded 8 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

**Table 10 |** Review findings, Haiku run.
Severity	From	Finding	Shown with the final answer
info	ruletext_style	The answer breaks the text rules (ASD-STE100) in 2 places. Sentence 11 uses the passive voice: "is set". Use the active voice. Sentence 16 uses the passive voice: "is set". Use the active voice.	yes
error	referee model	The report says no nodata value is set for the original and reprojected rasters. No logged step checks the nodata value. The count of 400 pixels comes from a width times height calculation, not from a valid-pixel count. The report must not state this without a logged source.	yes
warning	referee model	The report does not give a reason for the near resampling method. It must state the method and the reason for each reprojected raster.	yes
warning	referee model	The zone order was not verified. The read_file result shows only the byte count and no file content. The report still assumes zone A is the first square drawn. The report must not present this as checked.	yes
warning	referee model	The zonal statistics do not state the nodata handling or the CRS of the zones file. The report must state that statistics skip nodata pixels, or that the raster has no nodata value. It must also name the CRS of each polygon file it measures.	yes
info	referee model	The pixel values were reported as coming from gdalinfo with stats. The logged tool was raster_info. A gdalinfo command was only a manual route and was not run. The report must name the tool that made each number.	yes
info	referee model	The report names NAD27 and WGS 84 by name. The logs show only EPSG:26711 and EPSG:4326. These names are not in any logged result.	yes
info	referee model	The target CRS and the resampling method were set in the setup step, and the defaults list is empty. The log does not show the scientist approving the near method. The analyst must ask the scientist for these choices, not set them.	yes

Numbers in the answer

The last claim check read 20 numbers in the answer. 20 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

No tool call failed.

Data integrity

Each data file has the same SHA-256 hash now as at the time of the step that read it. The run did not change the data.

**Table 11 |** Data files and their SHA-256 hashes, Haiku run.
File	SHA-256	Fetched data	Steps with this hash
`{data}/rouault-gdal-autotest/byte.tif`736 bytes	`59ed6e9dd192`	the download script (fetch.sh) has no hash for this file	n1, n3, n4
`{data}/rouault-gdal-autotest/zones.geojson`472 bytes	`0d96b10244d0`	the download script (fetch.sh) has no hash for this file	n2, n4

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/rouault-gdal-autotest/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/rouault-gdal-autotest/bench.yaml.

cuvette bench papers --papers rouault-gdal-autotest --models claude:claude-haiku-5-5

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

raster_info (step n1)
Run: gdalinfo -stats <in>.tif
- <in>.tif
```
{data}/rouault-gdal-autotest/byte.tif
```
The manual route that the harness recorded
```
gdalinfo -stats {data}/rouault-gdal-autotest/byte.tif
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.
vector_info (step n2)
Run: ogrinfo -so -al <in>
- <in>
```
{data}/rouault-gdal-autotest/zones.geojson
```
The manual route that the harness recorded
```
ogrinfo -json -so -al {data}/rouault-gdal-autotest/zones.geojson
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.
reproject_raster (step n3)
Run: gdalwarp -t_srs <crs> -r <method> [-tr <size> <size>] [-dstnodata <value>] <in>.tif <out>.tif
- <in>.tif
```
{data}/rouault-gdal-autotest/byte.tif
```
- -t_srs = EPSG:4326
- -r = near
The manual route that the harness recorded
```
gdalwarp -overwrite -of GTiff -t_srs EPSG:4326 -r near {data}/rouault-gdal-autotest/byte.tif {work}/reproject_raster-1/result.tif
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.
zonal_stats (step n4)
QGIS: Processing Toolbox>Raster analysis > Zonal statistics. Code: rasterstats.zonal_stats(zones, raster, stats=[...], all_touched=<bool>)
- Raster layer
```
{data}/rouault-gdal-autotest/byte.tif
```
- Vector layer containing zones
```
{data}/rouault-gdal-autotest/zones.geojson
```
- all_touched = centers
- Note: The rasterstats code runs here. The QGIS dialog counts a pixel by its center, which matches centers. The all_touched setting has no switch in that dialog. The route in QGIS is not tested.
The manual route that the harness recorded
```
rasterstats.zonal_stats('{data}/rouault-gdal-autotest/zones.geojson', '{data}/rouault-gdal-autotest/byte.tif', all_touched=False, nodata=None)
```
The manual route uses the same method. The note in the route gives the known difference.

calculate (step n5)

Run the tool "calculate" with these settings: {"items":[{"name":"image_pixels","expression":"20*20"}]}.
- Code only: this step has no route in the program menus. Run it with the script or flow export.

The harness recorded no manual route for this step.

Figure

Run facts

**Table 12 |** Run facts, Haiku run.
Model	claude-haiku-5-5 through the Anthropic service
Date	2026-10-09 13:19:16 UTC
End of run	the model gave a final answer
Time	80 s
Requests to the model	8
Tokensunits of text that the model read and wrote	24 input, 7820 output, 116052 cache read, 22112 cache write
Cost estimate	$0.01 at list price, from the token counts
Tool calls	6 (0 failed)
Adapters	gdal 0.1.1, program 3.13.3
Session	`20261009-081915-58c9`

Code hash of each step (5)

**Table 13 |** Code hash of each step, Haiku run.
Step	Tool	Program version	Code hash
n1	`raster_info`	3.13.3	`dba2f5e14921`
n2	`vector_info`	3.13.3	`27441ebbe414`
n3	`reproject_raster`	3.13.3	`86820a0f856c`
n4	`zonal_stats`	3.13.3	`3f6d6ffc18ca`
n5	`calculate`	-	`d864d37ef90b`

The code hash is a fingerprint of the adapter name, the adapter version, the tool and its definition in the adapter. If one of these changes, the hash changes.

Model in this columnqwen3:8b · local model through Ollama 8 of 14 values match, 8 of 14 correct in the final answer

The session

setup The decision record starts with these values. A script set them before the first message, from the answers that we wrote before the run.

Target coordinate reference system (CRS): EPSG:4326Source in the tutorial or test suite: The request asks for latitude and longitude. The test suite has no warp step for this file, so we chose this CRS.
Resampling method: nearSource in the tutorial or test suite: Not in a source. We use the gdalwarp default. The warp is only a size check.
Nodata value: Source in the tutorial or test suite: The file has no nodata value. We set none.
Output pixel size: Source in the tutorial or test suite: Not in a source. We use the gdalwarp default.
Pixels that a zone edge cuts: centersSource in the tutorial or test suite: The request states this rule for the second square. It is also the default in rasterstats.

harness note A note from the harness to the model. The scientist did not write it.

- Setup values:
Geometry:
- Target coordinate reference system (target_crs): EPSG:4326
- Resampling method (resampling): near
- Output pixel size (pixel_size): 
Data:
- Nodata value (nodata): 
Zonal statistics:
- Pixels that a zone edge cuts (partial_pixels): centers
The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist.
- Mode is now watch: you run the steps.

The model calls raster_info (adapter gdal).

step n1 raster_info adapter gdal 0.1.1, GDAL 3.13.3

20 x 20 pixels, EPSG:26711, mean 126.765, stddev 22.928.

Input file: {data}/rouault-gdal-autotest/byte.tif SHA-256 59ed6e9dd192.

Arguments

path	`{data}/rouault-gdal-autotest/byte.tif`

Tool output

{
 "ok": true,
 "metrics": {
  "width": 20,
  "height": 20,
  "n_bands": 1,
  "pixel_x": 60,
  "pixel_y": 60,
  "origin_x": 440720,
  "origin_y": 3751320,
  "epsg": 26711,
  "min": 74,
  "max": 255,
  "mean": 126.765,
  "stddev": 22.928
 },
 "data": {
  "crs_name": "NAD27 / UTM zone 11N",
  "driver": "GTiff",
  "type": "Byte",
  "corner_ul": [
   440720,
   3751320
  ],
  "corner_lr": [
   441920,
   3750120
  ]
 },
 "manualRoute": "gdalinfo -stats {data}/rouault-gdal-autotest/byte.tif"
}

Model The model writes its answer. The claim check reads it. See the final answer.

claim check 8 numbers in the answer, 0 numbers without a source in the record.

Saved: results.xlsx (5 sheets). Folder: {work}

The final answer

The model wrote this answer. The harness shows it to the scientist after the review.

The raster is 20 pixels wide and 20 pixels high. It uses the NAD27 / UTM zone 11N projection (EPSG:26711). One pixel is 60 metres wide and 60 metres high. The lowest pixel value is 74, the highest is 255, the average is 126.765, and the standard deviation is 22.928.

Checks

Review findings

The review recorded 1 finding. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.

**Table 14 |** Review findings, qwen3:8b run.
Severity	From	Finding	Shown with the final answer
info	referee model	The pixel size is reported in metres, but the CRS is not a projected CRS. The area or distance from a geographic CRS such as EPSG:4326 should not be reported in metres.	yes

Numbers in the answer

The last claim check read 8 numbers in the answer. 8 numbers match a logged result. 0 numbers have no source in the record.

Deviations

The model did not try to change a choice of the scientist.

Failed tool calls

No tool call failed.

Data integrity

Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.

**Table 15 |** Data files and their SHA-256 hashes, qwen3:8b run.
File	SHA-256	Fetched data	Steps with this hash
`{data}/rouault-gdal-autotest/byte.tif`736 bytes	`59ed6e9dd192`	the download script (fetch.sh) has no hash for this file	n1
`{data}/rouault-gdal-autotest/zones.geojson`472 bytes	`0d96b10244d0`	the download script (fetch.sh) has no hash for this file	none

A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.

How to repeat it

Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.

CUVETTE_DATA={data} bash bench/papers/rouault-gdal-autotest/fetch.sh

Run the same case with Cuvette. The script gives the same answers from bench/papers/rouault-gdal-autotest/bench.yaml.

cuvette bench papers --papers rouault-gdal-autotest --models ollama:qwen3:8b

Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.

raster_info (step n1)
Run: gdalinfo -stats <in>.tif
- <in>.tif
```
{data}/rouault-gdal-autotest/byte.tif
```
The manual route that the harness recorded
```
gdalinfo -stats {data}/rouault-gdal-autotest/byte.tif
```
The manual route gives the same numbers. An automatic test in Cuvette checks this.

Figure

Run facts

**Table 16 |** Run facts, qwen3:8b run.
Model	qwen3:8b through Ollama, on our own computer
Date	2026-10-09 11:40:40 UTC
End of run	the model gave a final answer
Time	32 s
Requests to the model	2
Tokensunits of text that the model read and wrote	11420 input, 138 output, 0 cache read, 0 cache write
Cost estimate	none: the model runs on our own computer
Tool calls	1 (0 failed)
Adapters	gdal 0.1.1, program 3.13.3
Session	`20261009-064039-05ab`

Code hash of each step (1)

**Table 17 |** Code hash of each step, qwen3:8b run.
Step	Tool	Program version	Code hash
n1	`raster_info`	3.13.3	`dba2f5e14921`

The code hash is a fingerprint of the adapter name, the adapter version, the tool and its definition in the adapter. If one of these changes, the hash changes.

Rouault and the GDAL contributors: GDAL and OGR

The paper

What it measured

Data

The instruction

Results

Session records

The session

1. Size, projection and pixel size (from raster_info, which runs gdalinfo -stats)

2. Pixel values (from raster_info)

3. Image in latitude and longitude (from reproject_raster, which runs gdalwarp)

4. First square, A (from zonal_stats, which uses rasterstats)

5. Second square, B, pixel centers only (from zonal_stats, same run)

Check: all touched pixels (comparison run only)

What is uncertain

Your decisions

The final answer

1. Size, projection and pixel size (from raster_info, which runs gdalinfo -stats)

2. Pixel values (from raster_info)

3. Image in latitude and longitude (from reproject_raster, which runs gdalwarp)

4. First square, A (from zonal_stats, which uses rasterstats)

5. Second square, B, pixel centers only (from zonal_stats, same run)

Check only, not a result: all touched pixels

What is uncertain

Your decisions

Checks

Review findings

Numbers in the answer

Deviations

Failed tool calls

Data integrity

How to repeat it

Figure

Run facts

The session

The final answer

Corrected answer

Checks

Review findings

Numbers in the answer

Deviations

Failed tool calls

Data integrity

How to repeat it

Figure

Run facts

The session

The final answer

Checks

Review findings

Numbers in the answer

Deviations

Failed tool calls

Data integrity

How to repeat it

Figure

Run facts

The session

The final answer

Checks

Review findings

Numbers in the answer

Deviations

Failed tool calls

Data integrity

How to repeat it

Figure

Run facts

1. Size, projection and pixel size (from `raster_info`, which runs `gdalinfo -stats`)

2. Pixel values (from `raster_info`)

3. Image in latitude and longitude (from `reproject_raster`, which runs `gdalwarp`)

4. First square, A (from `zonal_stats`, which uses rasterstats)

5. Second square, B, pixel centers only (from `zonal_stats`, same run)

1. Size, projection and pixel size (from `raster_info`, which runs `gdalinfo -stats`)

2. Pixel values (from `raster_info`)

3. Image in latitude and longitude (from `reproject_raster`, which runs `gdalwarp`)

4. First square, A (from `zonal_stats`, which uses rasterstats)

5. Second square, B, pixel centers only (from `zonal_stats`, same run)