Show agricultural GDP contribution in Himalayan nations
The question
894468Show agricultural GDP contribution in Himalayan nations.
Exact submitted task and declared adaptations
Show agricultural GDP contribution in Himalayan nations.
Task conventions: Use the frozen country polygons and World Bank NV.AGR.TOTL.ZS 2022 column, in % of GDP. These are country-level indicators, not a subnational surface or a new regional aggregation. Join the supplied ISO_A3 to Country Code exactly. Nonmatching identifiers and missing measurements remain unknown; do not guess them or substitute another year. Retain every original country feature in the declared geography and its benchmark_row_id, including unknowns. No data must have a distinct map category, not zero. Create a quantitative choropleth with five quantile classes (fewer only if tied values collapse breaks), a visible legend with numeric bounds and units, and a neutral No data category. Values equal to a class break enter the upper class. Preserve negative and genuine zero values. This fixed classification and year are disclosed evaluation conventions; do not retrieve live replacements. Count original boundary features, not deduplicated sovereign states; preserve all source rows that meet the declared geography. Country-code sentinels such as -99 remain unmatched. Geography is fixed to original country features with NAME_EN in ["Nepal", "Bhutan", "India", "China", "Pakistan"]; retain all matching source geometries, and no others. Classification is calculated only over this geography. Membership reflects this disclosed benchmark edition, not current organization membership.
Add the resulting quantitative country layer to the map and retain an inspectable data artifact containing the original country geometry, benchmark_row_id, numeric value and class. End with one fenced JSON object: {count: countries with a known numeric value, unknown_count: countries without one, coverage_note: string, selection: {collectionId, itemId, assetKey}, value_field: numeric_column_name, class_field: classification_column_name, map_layer_id: delivered_layer_id}. The artifact must include known AND unknown countries, not only the known subset. Explain the year, units, key limitations and legend in the final response.
Delivery format: provide the answer object with all fields specified above as final_response on assess_result, alongside the inspected final artifact_refs. This replaces the JSON-code-block formatting requirement above. Blue attaches your explicit selection to the accepted final answer. Finish with a concise ordinary-language answer agreeing with that selected result; do not duplicate the JSON in chat.
Supplied immutable inputs:
[
{
"name": "World Bank country boundaries",
"edition": "GeoBenchX harmonized names",
"units": {},
"collectionId": "blue-generated--[resource id withheld]",
"itemId": "[resource id withheld]",
"assetKey": "data"
},
{
"name": "World Bank NV.AGR.TOTL.ZS",
"edition": "Frozen GeoBenchX World Development Indicators; use 2023",
"units": {
"2023": "% of GDP"
},
"collectionId": "blue-generated--[resource id withheld]",
"itemId": "[resource id withheld]",
"assetKey": "data"
}
]Outcome & verification
Independent artifact and delivery checks passed.
- Duration
- 15m 4s
- Recorded Blue tool calls
- 62
- Total tokens
- 2,770,945
- Model cost
- Not recorded
- Application revision
- d50497ea9084f29c91b05406688f87035df98d8a
- Release
- terra-geobenchx-v1
Recorded final answer
Independent checks
These checks are separate from the agent's own assessment. A successful job or an assertion of success is not enough to pass.
- Expected count
- 5
- Reported count
- 5
- Computation verified
- Yes
- Answer fulfilled
- Yes
Full grading and rubric record
{
"expectedCount": 5,
"actualCount": 5,
"expectedMetrics": null,
"actualMetrics": null,
"computationCorrect": true,
"answerFulfilled": true,
"adjudication": null,
"qualification": null,
"reasons": []
}Recorded investigation findings
The agent's reported hypotheses and assessments, retained as evidence of its process—not independent proof that the answer is correct. Failed calls retain their requested verdict, not an accepted assessment.
run_trial · completed
Does the exact benchmark method preserve the five named country rows, join the frozen World Bank table on ISO_A3 = Country Code, keep unknowns as null, and produce the requested 2022 quantile choropleth classification with a separate No data class?
assess_trial · completed · assessment: sufficient
The private trial retained exactly 5 benchmark country rows with the requested names, preserved benchmark_row_id, joined all 5 rows by exact ISO_A3 = Country Code, and used the frozen 2022 NV.AGR.TOTL.ZS values in % of GDP. The classifier produced 5 quantile classes over the 5 known values, with class 0 reserved for No data and missingCount 0 in this benchmark subset. The published category labels and boundary convention place break-equal values in the upper class.
assess_result · completed · assessment: sufficient
The final production output contains the 5 benchmark country features for Bhutan, China, India, Nepal, and Pakistan, with preserved benchmark_row_id, exact ISO_A3-to-Country Code joins, the 2022 NV.AGR.TOTL.ZS value in % of GDP, and five quantile classes. The quantitative layer is on the map, and the legend shows five numeric class ranges plus a neutral No data category. In this fixed benchmark geography all 5 countries have known 2022 values, so the No data class is available but unused.
Recorded workflow
- 01
Frozen agricultural GDP table
Recorded source · succeeded
- 02
Classify agricultural GDP share
Recorded vector-classify · succeeded
- 03
Frozen country boundaries
Recorded source · succeeded
- 04
Filter named benchmark country rows
Recorded vector-filter · succeeded
- 05
Agricultural GDP contribution by country (2022)
Recorded output · succeeded
- 06
Join World Bank 2022 values
Recorded table-attribute-join · succeeded
- 07
Create named 2022 value field
Recorded vector-field-calculate · succeeded
- 08
Frozen agricultural GDP table
Recorded source · succeeded
- 09
Classify agricultural GDP share
Recorded vector-classify · succeeded
- 10
Frozen country boundaries
Recorded source · succeeded
- 11
Filter named benchmark country rows
Recorded vector-filter · succeeded
- 12
Join World Bank 2022 values
Recorded table-attribute-join · succeeded
- 13
Filter named benchmark country rows
Recorded output · succeeded
- 14
Join World Bank 2022 values
Recorded output · succeeded
- 15
Classify agricultural GDP share
Recorded output · succeeded
- 16
Create named 2022 value field
Recorded vector-field-calculate · succeeded
Activity
Recorded actionsRecorded observable actions only, not private reasoning. Raw payloads, geometry, private links and credentials are omitted; full evidence is retained privately. Activity/workflow lists bounded at 500 entries.
Progress update
I’ll inspect the saved analysis context and the two supplied datasets first, then build a small tested method that keeps the original country rows and marks unmatched or missing values as “No data.”
Progress update
I found the Blue workflow tools. Next I’m checking the saved brief and the two supplied snapshots so I can use the exact boundary rows and the exact indicator column you specified, without substituting years or country matches.
get_analysis
Recorded tool call · completed
get_collection
Recorded tool call · completed
get_collection
Recorded tool call · completed
get_item
Recorded tool call · completed
get_item
Recorded tool call · completed
inspect_artifact
Recorded tool call · failed
inspect_artifact
Recorded tool call · failed
list_processors
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
Progress update
I confirmed the frozen indicator snapshot really has both 2022 and 2023 columns. The next step is a small private test that reads just the supplied rows, filters the five named countries, and checks the exact join and quantile classification before I save the production workflow.
describe_processors
Recorded tool call · completed
describe_processors
Recorded tool call · completed
describe_processors
Recorded tool call · completed
describe_processors
Recorded tool call · completed
Progress update
I have the core method now: filter the supplied boundaries to the five named country rows, left-join the frozen World Bank table on `ISO_A3 = Country Code`, then classify the 2022 value field with a five-quantile country choropleth so unknowns stay as a separate “No data” class. I’m checking the workflow schema next so the saved draft delivers both the inspectable artifact and the map layer cleanly.
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
update_analysis_brief
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
run_trial
Recorded tool call · completed
Progress update
The method test finished and it worked on the supplied frozen data: five country rows were kept, all five joined by exact country code, and the 2022 values classified cleanly. I’m inspecting the tested output now so I can save the final workflow, add the derived layer to the map, and deliver the exact artifact reference.
get_trial_report
Recorded tool call · completed
inspect_artifact
Recorded tool call · completed
inspect_artifact
Recorded tool call · completed
Progress update
I’ve already proved the method on the frozen benchmark inputs. Now I’m pulling the exact row values, class field, and processor receipt so the delivered layer points to the derived country dataset, not just the private trial check.
inspect_artifact
Recorded tool call · completed
inspect_workflow_results
Recorded tool call · completed
inspect_artifact
Recorded tool call · completed
Progress update
I already have the five country values and classes. I’m checking the classifier’s summary next so I can verify the legend ranges and the neutral “No data” behavior before I publish the final layer.
inspect_artifact
Recorded tool call · completed
get_workflow_run
Recorded tool call · completed
Progress update
The classification summary is clear: five known values, five quantile classes, missing count 0, and class `0` reserved for “No data.” I’m identifying the displayable output now so I can bind the derived choropleth to the map instead of just keeping it as a table.
list_processor_jobs
Recorded tool call · completed
list_map_layers
Recorded tool call · completed
get_collection
Recorded tool call · completed
get_item
Recorded tool call · completed
assess_trial
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
Progress update
The tested method is now stable. I’m saving it as the production workflow with the same exact filter, join key, year, and five-quantile classification so the final layer can reuse the verified computation instead of rebuilding a different one.
Progress update
I’ve saved the tested method as a workflow draft. If validation succeeds cleanly, I’ll run it and then bind the derived classified country output to the map.
propose_workflow
Recorded tool call · completed
Progress update
The saved draft is close, but Blue blocked it because the copied trial still carries a world-sized scope while the contract names the five-country study area. I’m correcting that bookkeeping so the runnable workflow matches the actual geography of the task.
get_workflow
Recorded tool call · completed
get_workflow
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
Progress update
I found the saved node areas. They still use the copied world snapshot extent, so I’m replacing those with the actual five-country envelope and matching contract scope in one revision.
get_workflow
Recorded tool call · completed
get_workflow
Recorded tool call · completed
revise_workflow
Recorded tool call · completed
execute_workflow
Recorded tool call · completed
Progress update
The production run appears to have finished in the background. I’m verifying the final output receipts and legend summary from that run now, then I’ll bind the derived country layer to the map and record the final answer object.
Progress update
I’ve got the approved workflow revision and the completed run receipt. Next I’m inspecting the final derived item itself, not the earlier trial copy, so the delivered selection and map layer point to the exact production output.
get_workflow_run
Recorded tool call · completed
inspect_workflow_results
Recorded tool call · completed
Progress update
I’ve got the final run’s item ID and map layer ID. I’m checking the final derived item’s rows and legend metadata one last time so the accepted result points to the exact displayed output.
inspect_artifact
Recorded tool call · completed
inspect_artifact
Recorded tool call · completed
get_item
Recorded tool call · completed
list_map_layers
Recorded tool call · completed
Progress update
The final output already has a verified map receipt from the production run. I’m just fetching the finished preview receipt and checking the active map layer list once more so the final assessment references the exact displayed artifact.
inspect_artifact
Recorded tool call · completed
list_map_layers
Recorded tool call · completed
Progress update
I’ve verified the final production artifact, legend, and live map layer. I’m refreshing the brief notes so they match the completed work, then I’ll record the accepted final result object.
update_analysis_brief
Recorded tool call · completed
Progress update
I’ve verified the exact final artifact, the classifier summary, and the live map layer. I’m recording the accepted result now with the explicit selection, field names, counts, and map layer ID you asked for.
assess_result
Recorded tool call · completed