Research/Terra/ 917242
Task evidence / country-choropleth

Show rural population distribution across countries

PassComputational taskUnpublished draft
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The question

917242
Show rural population distribution across countries.
Exact submitted task and declared adaptations
Show rural population distribution across countries.

Task conventions: Use the frozen country polygons and World Bank SP.RUR.TOTL 2023 column, in people. 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 and 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.



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 SP.RUR.TOTL",
    "edition": "Frozen GeoBenchX World Development Indicators; use 2023",
    "units": {
      "2023": "people"
    },
    "collectionId": "blue-generated--[resource id withheld]",
    "itemId": "[resource id withheld]",
    "assetKey": "data"
  }
]

Outcome & verification

Independent artifact and delivery checks passed.

Duration
16m 31s
Recorded Blue tool calls
53
Total tokens
3,116,140
Model cost
Not recorded
Application revision
d50497ea9084f29c91b05406688f87035df98d8a
Release
terra-geobenchx-v1

Recorded final answer

Done — I added **Rural population by country, 2023** to the map. It uses the frozen **World Bank SP.RUR.TOTL 2023** values in **people**, joined exactly from **Country Code** to **ISO_A3** on the supplied country polygons. The legend has **five quantile classes** with breaks at **94,356**, **971,992**, **3,378,005**, and **12,534,572 people**, plus a separate **No data** category. Important limitation: this is a **country-level** map only, not a subnational rural population surface. **35 features** remain **No data** because the exact code match is unavailable or the frozen 2023 value is missing. Next: click countries on the map or inspect **benchmark_row_id**, **2023**, and **rural_pop_class** in the delivered artifact. Sources: supplied **World Bank country boundaries** (GeoBenchX harmonized names) and frozen **World Bank WDI SP.RUR.TOTL 2023** snapshots, Blue-hosted and retrieved in this run on **2026-10-07**. ### Limitations - This result is a country-level indicator map. It does not show subnational rural population distribution or any new regional aggregation. - 35 features remain No data where the exact ISO_A3 to Country Code match is unavailable or the frozen 2023 value is missing. - This is a country-level indicator map. It does not show subnational rural population distribution or any new regional aggregation.

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
216
Reported count
216
Computation verified
Yes
Answer fulfilled
Yes
Full grading and rubric record
{
  "expectedCount": 216,
  "actualCount": 216,
  "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

Do the supplied frozen boundary and indicator snapshots expose the exact fields needed for an ISO_A3 to Country Code join, retained benchmark_row_id, and a 2023 rural population value in people?

assess_trial · completed · assessment: sufficient

The boundary snapshot exposes geometry, ISO_A3, and benchmark_row_id for 251 features. The indicator table exposes Country Code and a numeric 2023 field for 266 records. My initial expectation that the indicator table might also contain benchmark_row_id was wrong; it does not, but that field only needs to be retained from the boundary side. The boundary sample and statistics show repeated ISO_A3 values and seven '-99' codes, while the table includes aggregate rows such as WLD and AFE that are not country polygons. An exact left join remains feasible and should preserve all boundary features while leaving nonmatching or missing measurements unknown.

run_trial · completed

Does an exact left join from ISO_A3 to Country Code preserve every supplied country feature and benchmark_row_id, keep nonmatches unknown, and classify the joined 2023 rural population values into five quantile classes with a separate No data category?

assess_trial · completed · assessment: sufficient

The exact left join preserved all 251 boundary features and all benchmark_row_id values. Producer observations show 217 boundary features matched at least one table row and 34 remained unmatched; the joined 2023 field is null for 35 features, so one matched row has a missing 2023 value in the frozen table. The classified output retains all 251 features, keeps the numeric 2023 field in people, and adds rural_pop_class with values 0 through 5. Class 0 is the distinct No data category, and classes 1 to 5 cover known values. The preview and statistics confirm the output keeps geometry, benchmark_row_id, the numeric value, and the class field needed for delivery.

assess_result · completed · assessment: sufficient

I delivered the requested country-level choropleth from the supplied frozen inputs and added it to the map. The final derived artifact keeps all 251 original country features and benchmark_row_id values, joins the frozen World Bank SP.RUR.TOTL 2023 values in people by exact ISO_A3-to-Country Code match, and classifies known values into five quantile classes using rural_pop_class while keeping class 0 as No data. The classifier summary shows 216 countries with known numeric values and 35 countries or special areas remaining No data under the required exact join. Legend break values are 94,356; 971,992; 3,378,005; and 12,534,572 people.

Recorded workflow

  1. 01

    Classify joined 2023 values

    Recorded vector-classify · succeeded

  2. 02

    Supplied country boundaries

    Recorded source · succeeded

  3. 03

    Rural population by country, 2023

    Recorded output · succeeded

  4. 04

    Join 2023 rural population

    Recorded table-attribute-join · succeeded

  5. 05

    Supplied rural population table

    Recorded source · succeeded

  6. 06

    Classify joined 2023 values

    Recorded vector-classify · succeeded

  7. 07

    Supplied country boundaries

    Recorded source · succeeded

  8. 08

    Join 2023 rural population

    Recorded table-attribute-join · succeeded

  9. 09

    Supplied rural population table

    Recorded source · succeeded

  10. 10

    Join 2023 rural population

    Recorded output · succeeded

  11. 11

    Classify joined 2023 values

    Recorded output · succeeded

  12. 12

    Supplied country boundaries

    Recorded source · succeeded

  13. 13

    Supplied rural population table

    Recorded source · succeeded

  14. 14

    Supplied country boundaries

    Recorded output · succeeded

  15. 15

    Supplied rural population table

    Recorded output · succeeded

Activity

Recorded actions

Recorded 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.

  1. Progress update

    I’m checking the saved analysis context and the exact tools for a fixed country join, quantile classes, and adding the final layer to your map. Then I’ll test the join/classification method on the supplied frozen inputs before saving the production workflow.

  2. get_analysis

    Recorded tool call · completed

  3. get_map_context

    Recorded tool call · completed

  4. Progress update

    I found no saved brief yet. Next I’m inspecting the two frozen inputs and recording the exact output checks: keep every original country feature, join ISO_A3 to Country Code exactly, use 2023 people values, classify known values into five quantiles with a separate No data category, and add that derived layer to the map.

  5. update_analysis_brief

    Recorded tool call · completed

  6. get_collection

    Recorded tool call · completed

  7. get_collection

    Recorded tool call · completed

  8. Progress update

    I confirmed both frozen inputs exist and are the intended snapshots. The next step is to inspect their real fields and a few rows so I can test the exact key join and classification without guessing column names.

  9. inspect_artifact

    Recorded tool call · failed

  10. inspect_artifact

    Recorded tool call · failed

  11. get_tool_help

    Recorded tool call · completed

  12. get_tool_help

    Recorded tool call · completed

  13. list_processors

    Recorded tool call · completed

  14. list_items

    Recorded tool call · completed

  15. list_items

    Recorded tool call · completed

  16. get_tool_help

    Recorded tool call · completed

  17. get_collection

    Recorded tool call · completed

  18. get_collection

    Recorded tool call · completed

  19. get_tool_help

    Recorded tool call · completed

  20. Progress update

    I have the exact trial input shape now. I’m running the smallest useful experiment: load both supplied snapshots as sources, inspect their actual schemas and sample rows, and confirm the exact join keys and 2023 value field before I build the production workflow.

  21. run_trial

    Recorded tool call · completed

  22. update_analysis_brief

    Recorded tool call · completed

  23. Progress update

    The source check finished in the background. I’m using that saved trial instead of rerunning it, and now I’m drilling into the trial outputs to confirm the exact field names for the 2023 join and the fixed quantile classification.

  24. inspect_artifact

    Recorded tool call · completed

  25. inspect_artifact

    Recorded tool call · completed

  26. get_trial_report

    Recorded tool call · completed

  27. Progress update

    The schema inspections finished in the background too. I’m reading those exact results now, then I’ll test one end-to-end join-plus-classify trial on the supplied snapshots before I save the production workflow.

  28. inspect_artifact

    Recorded tool call · completed

  29. inspect_artifact

    Recorded tool call · completed

  30. get_trial_report

    Recorded tool call · completed

  31. Progress update

    I’ve confirmed the key fields exist: the country file has `ISO_A3` and `benchmark_row_id`, and the indicator table has `Country Code` plus a numeric `2023` field. I’m pulling a few actual rows next so the join uses the exact frozen field names, then I’ll test the join and five-quantile classification itself.

  32. inspect_artifact

    Recorded tool call · completed

  33. inspect_artifact

    Recorded tool call · completed

  34. describe_processors

    Recorded tool call · completed

  35. Progress update

    The source setup is still good. I’m reusing the finished background inspections and reading the exact row samples plus the join/classify processor schemas, so the production method uses the frozen fields exactly and keeps unknown countries intact.

  36. inspect_artifact

    Recorded tool call · completed

  37. inspect_artifact

    Recorded tool call · completed

  38. describe_processors

    Recorded tool call · completed

  39. Progress update

    I confirmed the fixed fields and also found the main real limitation: the boundary snapshot has some repeated or `-99` ISO codes, while the indicator table includes non-country aggregates like `WLD` and `AFE`. That means an exact left join should keep every boundary row, but some countries or special areas will stay unknown by design. I’m recording that and moving to the full join-and-classify test.

  40. assess_trial

    Recorded tool call · completed

  41. Progress update

    I’ve now moved past schema checks. The next test is the real method: exact left join from `ISO_A3` to `Country Code`, then five-quantile classification of the joined `2023` values. If that holds at world scale, I can promote the same computation into the final map workflow without changing the rules.

  42. run_trial

    Recorded tool call · completed

  43. Progress update

    The world-scale method trial finished successfully in the background. It kept all 251 boundary features, attached the frozen `2023` values where exact codes matched, and left 35 features unknown. I’m saving that tested computation as the final workflow now, then I’ll run it, add the verified layer to your map, and assess the delivered artifact.

  44. inspect_artifact

    Recorded tool call · completed

  45. inspect_artifact

    Recorded tool call · completed

  46. Progress update

    The method is tested. I’m using the finished classified trial output to confirm the preview and class field, then I’ll save the production workflow from that exact computation so the final layer uses the same verified join and quantile rules.

  47. Progress update

    The full trial answered the main method question. I’m recording its exact findings now: 251 boundary features retained, 216 known `2023` values, 35 unknowns in the final classified output, and a `rural_pop_class` field with `0` reserved for No data and `1–5` for quantiles.

  48. assess_trial

    Recorded tool call · completed

  49. update_analysis_brief

    Recorded tool call · completed

  50. get_tool_help

    Recorded tool call · completed

  51. get_tool_help

    Recorded tool call · completed

  52. get_tool_help

    Recorded tool call · completed

  53. get_tool_help

    Recorded tool call · completed

  54. get_tool_help

    Recorded tool call · completed

  55. get_tool_help

    Recorded tool call · completed

  56. Progress update

    I’ve saved the tested method details. Next I’m turning that exact trial into the production workflow with the fixed join keys, fixed year, and fixed quantile rule preserved in the workflow contract.

  57. propose_workflow

    Recorded tool call · completed

  58. get_tool_help

    Recorded tool call · completed

  59. Progress update

    The draft saved, and Blue told me the exact repair: the contract’s fixed parameter paths must be relative to each processor’s inputs. I’m correcting only those paths and revalidating the saved workflow instead of rebuilding it.

  60. Progress update

    I’m refreshing the saved draft with the exact corrected parameter paths Blue asked for. Nothing else about the tested method is changing.

  61. propose_workflow

    Recorded tool call · completed

  62. execute_workflow

    Recorded tool call · completed

  63. Progress update

    The workflow is now validated and ready. I’m starting that saved production run, then I’ll inspect its output, add the verified country layer to your map, and finish the final result check.

  64. inspect_workflow_results

    Recorded tool call · completed

  65. Progress update

    The map output is already bound: the production receipt shows the derived country layer was added as `Rural population by country, 2023`. I’m pulling the final artifact preview and the classifier summary now so I can verify the legend bounds, counts, and selected output fields before I record the final result.

  66. inspect_artifact

    Recorded tool call · completed

  67. list_map_layers

    Recorded tool call · completed

  68. inspect_artifact

    Recorded tool call · completed

  69. inspect_artifact

    Recorded tool call · completed

  70. Progress update

    I have the class summary already: 216 known country values, 35 No data, five quantile classes, and break values in people. I’m fetching the final deliverable preview now so I can confirm the retained fields on the delivered artifact before I lock the result.

  71. Progress update

    I’ve verified the delivered artifact, its class summary, and the bound map layer. Now I’m recording the final selected result object with the delivered layer ID and the derived artifact selection that’s actually on your map.

  72. assess_result

    Recorded tool call · completed

  73. Progress update

    The result is now accepted. I’m clearing the stale “still needs execution” note so the saved brief matches the delivered layer and remaining limitation.

  74. update_analysis_brief

    Recorded tool call · completed

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