Experimental run record

Run 90a4a241

An inspectable execution record: configuration, deterministic outcomes, attempts, artifacts, and failure traces. This record is not a comparison.

· pack geoworkbench-0.1 · accepted

Trials
12
Accepted
8
Configurations
4
Tasks
3
Cost USD
0.02152

Trials

attempt traces appear beneath each trial
Trial outcomes are deterministic verifier results, not model-judge opinions.
TaskConfigurationRetryResultAttemptsCostLatency
crs-reproject-munichGLM-4.7-flash (Workers AI)single-shotaccepted10.0015232054
# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — accepted — cost 0.00152 — 32054ms
# verifier: PASS — CRS reproject to WGS84 verified
# artifact: download (591 bytes)
import geopandas as gpd

# Load the input data
gdf = gpd.read_file("input.geojson")

# Diagnose CRS: Large coordinates indicate Web Mercator (EPSG:3857)
gdf.crs = "EPSG:3857"

# Reproject to WGS84 (EPSG:4326)
gdf_wgs84 = gdf.to_crs("EPSG:4326")

# Write the result
gdf_wgs84.to_file("output.geojson", driver="GeoJSON")
crs-reproject-munichGLM-4.7-flash (Workers AI)single-retryaccepted10.0012626040
# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — accepted — cost 0.00126 — 26040ms
# verifier: PASS — CRS reproject to WGS84 verified
# artifact: download (591 bytes)
import geopandas as gpd

# Load the input file
gdf = gpd.read_file("input.geojson")

# Diagnose and assign CRS
# The prompt indicates coordinates are large (millions), indicating a projected CRS.
# We are instructed to determine the CRS is EPSG:3857.
gdf.crs = "EPSG:3857"

# Reproject to WGS84 (EPSG:4326)
gdf_wgs84 = gdf.to_crs("EPSG:4326")

# Write the result to output.geojson
gdf_wgs84.to_file("output.geojson", driver="GeoJSON")
raster-zonal-meanGLM-4.7-flash (Workers AI)single-shotrejected10.0019055953
# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — rejected — cost 0.00190 — 55953ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

raster-zonal-meanGLM-4.7-flash (Workers AI)single-retryrejected20.0038287049
# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — rejected — cost 0.00190 — 38014ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — rejected — cost 0.00192 — 87049ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

vector-buffer-1kmGLM-4.7-flash (Workers AI)single-shotaccepted10.0010323259
# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — accepted — cost 0.00103 — 23259ms
# verifier: PASS — 1000m buffers verified
# artifact: download (5935 bytes)
import geopandas as gpd

gdf = gpd.read_file("sites.geojson")
gdf_projected = gdf.to_crs(epsg=3857)
gdf_projected['geometry'] = gdf_projected.buffer(1000)
gdf_final = gdf_projected.to_crs(epsg=4326)
gdf_final.to_file("buffers.geojson", driver="GeoJSON")
vector-buffer-1kmGLM-4.7-flash (Workers AI)single-retryaccepted10.0012426260
# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — accepted — cost 0.00124 — 26260ms
# verifier: PASS — 1000m buffers verified
# artifact: download (5903 bytes)
import geopandas as gpd

# Read the input file
gdf = gpd.read_file("sites.geojson")

# Reproject to EPSG:3857 (Web Mercator) for accurate 1000m buffering
gdf_projected = gdf.to_crs(epsg=3857)

# Create 1000m buffers
gdf_projected['buffer'] = gdf_projected.geometry.buffer(1000)

# Extract the buffer geometries and reproject back to EPSG:4326
buffers_gdf = gpd.GeoDataFrame(geometry=gdf_projected['buffer'], crs="EPSG:3857")
buffers_gdf = buffers_gdf.to_crs(epsg=4326)

# Write the output
buffers_gdf.to_file("buffers.geojson", driver="GeoJSON")
crs-reproject-munichKimi K2.6 (Workers AI)single-shotaccepted10.0012431950
# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — accepted — cost 0.00124 — 31950ms
# verifier: PASS — CRS reproject to WGS84 verified
# artifact: download (591 bytes)
import geopandas as gpd

gdf = gpd.read_file("input.geojson")
gdf = gdf.set_crs(epsg=3857, allow_override=True)
gdf = gdf.to_crs(epsg=4326)
gdf.to_file("output.geojson", driver="GeoJSON")
crs-reproject-munichKimi K2.6 (Workers AI)single-retryaccepted10.0016944513
# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — accepted — cost 0.00169 — 44513ms
# verifier: PASS — CRS reproject to WGS84 verified
# artifact: download (591 bytes)
import geopandas as gpd

gdf = gpd.read_file("input.geojson")
gdf = gdf.set_crs(epsg=3857, allow_override=True)
gdf = gdf.to_crs(epsg=4326)
gdf.to_file("output.geojson", driver="GeoJSON")
raster-zonal-meanKimi K2.6 (Workers AI)single-shotrejected10.0019047663
# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — rejected — cost 0.00190 — 47663ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

raster-zonal-meanKimi K2.6 (Workers AI)single-retryrejected20.0038385304
# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — rejected — cost 0.00190 — 38181ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — rejected — cost 0.00193 — 85304ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

vector-buffer-1kmKimi K2.6 (Workers AI)single-shotaccepted10.0011330670
# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — accepted — cost 0.00113 — 30670ms
# verifier: PASS — 1000m buffers verified
# artifact: download (5935 bytes)
import geopandas as gpd

gdf = gpd.read_file("sites.geojson")
gdf = gdf.set_crs("EPSG:4326", allow_override=True)
gdf_proj = gdf.to_crs("EPSG:3857")
gdf_proj["geometry"] = gdf_proj.buffer(1000)
gdf_out = gdf_proj.to_crs("EPSG:4326")
gdf_out.to_file("buffers.geojson", driver="GeoJSON")
vector-buffer-1kmKimi K2.6 (Workers AI)single-retryaccepted10.0009724427
# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — accepted — cost 0.00097 — 24427ms
# verifier: PASS — 1000m buffers verified
# artifact: download (5935 bytes)
import geopandas as gpd

gdf = gpd.read_file("sites.geojson")
if gdf.crs is None:
    gdf.set_crs(epsg=4326, inplace=True)

gdf_proj = gdf.to_crs(epsg=3857)
gdf_proj["geometry"] = gdf_proj.buffer(1000)

gdf_wgs84 = gdf_proj.to_crs(epsg=4326)
gdf_wgs84.to_file("buffers.geojson", driver="GeoJSON")