Research/Terra/ 854050
Task evidence / bivariate-map

Visualize at one map rural population percentage (calculated from total and rural population) and agriculture value added as % of GDP for 2019.

PassComputational taskUnpublished draft
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The question

854050
Visualize at one map rural population percentage (calculated from total and rural population) and agriculture value added as % of GDP for 2019.
Exact submitted task and declared adaptations
Visualize at one map rural population percentage (calculated from total and rural population) and agriculture value added as % of GDP for 2019.

Task conventions: Use the frozen country boundaries and the 2019 column of every supplied indicator. X is rural (% of population); Y is agriculture (% of GDP). X is 100 times rural population divided by total population. Join ISO_A3 to Country Code exactly. Keep every original country feature and benchmark_row_id, including unknowns and repeated country identities. Do not guess missing values or substitute years. A missing numerator or missing/zero denominator is unknown. Make one bivariate choropleth: three quantile classes on each axis, computed over rows where BOTH measurements are known. Collapse tied breaks; equality enters the upper class. Combined class is (yClass-1)*xClasses+xClass with 1-based axes. Missing either measurement is neutral class zero. Retain the numeric X and Y values even when only one is missing. The legend must distinguish joint classes with both ranges and units. These are disclosed evaluation conventions, not live-data replacements or proof of causation.



Add the quantitative joint-class layer to the map and retain its complete vector artifact. End with one fenced JSON object: {count: rows with both measurements known, unknown_count: rows missing either, coverage_note: string, selection: {collectionId,itemId,assetKey}, value_field: X numeric column, y_value_field: Y numeric column, class_field: joint class column, map_layer_id: delivered layer ID}. Explain year, units, missing coverage and the relationship without claiming causality.

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"
  },
  {
    "name": "World Bank SP.POP.TOTL",
    "edition": "Frozen GeoBenchX World Development Indicators; use 2023",
    "units": {
      "2023": "people"
    },
    "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
20m 25s
Recorded Blue tool calls
62
Total tokens
3,061,831
Model cost
Not recorded
Application revision
d50497ea9084f29c91b05406688f87035df98d8a
Release
terra-geobenchx-v1

Recorded final answer

Done — the 2019 bivariate world layer is on your map. It uses the supplied frozen World Bank country boundaries and 2019 World Development Indicators tables for rural population, total population, and agriculture value added. X is rural population as % of population, Y is agriculture value added as % of GDP; 196 preserved features have both 2019 measures, and 55 preserved features stay in neutral class 0 because one or both 2019 inputs are unknown. This shows a 2019 relationship only and does not imply causation. ### Limitations - The bivariate classes are computed from the 196 preserved features where both 2019 measurements are known; 55 preserved features remain neutral class 0 because one or both supplied 2019 values are unknown after the exact join. - The map shows a descriptive relationship between the two 2019 measures and does not imply causation. - 55 preserved features remain neutral class 0 because one or both supplied 2019 values are unknown after the exact join. - 35 preserved features have unknown rural population percentage because rural population is missing, total population is missing, or total population is zero after the exact join. - The bivariate map describes a 2019 relationship between the two measures and does not imply causation. - The workflow preserves repeated identities and unmatched special territories exactly as supplied. - Unknown rural share is preserved when rural population is missing, total population is missing, or total population is zero. - The workflow preserves unknown 2019 agriculture values rather than imputing them. - Joint classes are computed only from preserved features where both measures are known. - The map is descriptive and does not imply causation.

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
196
Reported count
196
Computation verified
Yes
Answer fulfilled
Yes
Full grading and rubric record
{
  "expectedCount": 196,
  "actualCount": 196,
  "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

Can I sequentially join the supplied rural and total population tables when both use the same year column name, while preserving every boundary feature and exact ISO_A3 to Country Code matching?

assess_trial · completed · assessment: sufficient

The second join succeeded and preserved all 251 boundary features. Blue kept the repeated year columns as distinct fields, with rural population in 2019_x and total population in 2019_y in the tested output. The exact ISO_A3 to Country Code left join matched 217 input features and left 34 unmatched as unknowns, which is consistent with preserving every original boundary feature and repeated country identities.

run_trial · completed

Does the full-world method produce the requested 2019 rural population percentage, 2019 agriculture % of GDP, and neutral-class bivariate choropleth while preserving every supplied country feature and benchmark_row_id?

assess_trial · completed · assessment: sufficient

The full-world method produced the requested fields and preserved all 251 supplied boundary features and benchmark_row_id values. It calculated rural_pct_2019 in % of population and agri_pct_gdp_2019 in % of GDP from the supplied 2019 columns, kept unknowns without year substitution, and produced joint_class_2019 with neutral class 0 and positive classes 1 through 9. Exact inspection showed 196 preserved features with both measurements known and 55 preserved features with at least one unknown measurement, so those 55 features correctly remained class 0.

assess_result · failed · requested assessment: sufficient

I added the final 2019 world rural-agriculture bivariate layer to the map and retained its complete vector artifact. The final artifact preserves all 251 supplied country features and benchmark_row_id values. It classifies 196 preserved features where both 2019 measures are known into classes 1 through 9 and leaves 55 preserved features in neutral class 0 where one or both 2019 inputs are unknown. The final classifier summary reports quantile breaks at 26.15099448647741 and 48.84299998644398 for rural population (% of population) and 2.86077493680796 and 10.8113571045068 for agriculture value added (% of GDP).

assess_result · completed · assessment: sufficient

I added the final 2019 world rural-agriculture bivariate layer to the map and retained its complete vector artifact. The final artifact preserves all 251 supplied country features and benchmark_row_id values. It classifies 196 preserved features where both 2019 measures are known into classes 1 through 9 and leaves 55 preserved features in neutral class 0 where one or both 2019 inputs are unknown. The final classifier summary reports quantile breaks at 26.15099448647741 and 48.84299998644398 for rural population (% of population) and 2.86077493680796 and 10.8113571045068 for agriculture value added (% of GDP).

Recorded workflow

  1. 01

    Agriculture share table

    Recorded source · succeeded

  2. 02

    Frozen country boundaries

    Recorded source · succeeded

  3. 03

    Calculate 2019 X and Y values

    Recorded vector-field-calculate · succeeded

  4. 04

    Classify 2019 rural and agriculture shares

    Recorded vector-bivariate-classify · succeeded

  5. 05

    2019 world rural-agriculture bivariate map

    Recorded output · succeeded

  6. 06

    Join agriculture 2019

    Recorded table-attribute-join · succeeded

  7. 07

    Join rural 2019

    Recorded table-attribute-join · succeeded

  8. 08

    Join total 2019

    Recorded table-attribute-join · succeeded

  9. 09

    Rural population table

    Recorded source · succeeded

  10. 10

    Total population table

    Recorded source · succeeded

  11. 11

    Agriculture share table

    Recorded source · succeeded

  12. 12

    Frozen country boundaries

    Recorded source · succeeded

  13. 13

    Calculate 2019 X and Y values

    Recorded vector-field-calculate · succeeded

  14. 14

    Classify 2019 rural and agriculture shares

    Recorded vector-bivariate-classify · succeeded

  15. 15

    Join agriculture 2019

    Recorded table-attribute-join · succeeded

  16. 16

    Join rural 2019

    Recorded table-attribute-join · succeeded

  17. 17

    Join total 2019

    Recorded table-attribute-join · succeeded

  18. 18

    Rural population table

    Recorded source · succeeded

  19. 19

    Total population table

    Recorded source · succeeded

  20. 20

    Join agriculture 2019

    Recorded output · succeeded

  21. 21

    Classify 2019 rural and agriculture shares

    Recorded output · succeeded

  22. 22

    Frozen country boundaries

    Recorded source · succeeded

  23. 23

    Join rural 2019

    Recorded table-attribute-join · succeeded

  24. 24

    Join total 2019

    Recorded table-attribute-join · succeeded

  25. 25

    Rural population table

    Recorded source · succeeded

  26. 26

    Total population table

    Recorded source · succeeded

  27. 27

    Join total 2019

    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’ll build the method first, test the joins and classing on the supplied files, then save a workflow that adds the final bivariate layer to your map.

  2. Progress update

    I found the Blue analysis tools. Next I’m checking the supplied files and the available join/calculation processors so I can test the exact 2019 join and class rules before saving the final map workflow.

  3. get_analysis

    Recorded tool call · completed

  4. get_collection

    Recorded tool call · completed

  5. get_collection

    Recorded tool call · completed

  6. get_collection

    Recorded tool call · completed

  7. get_collection

    Recorded tool call · completed

  8. list_processors

    Recorded tool call · completed

  9. Progress update

    I have the right processor family: exact key join, field calculation, and a built-in bivariate classifier that already matches the tie and class formula rules. I’m recording the analysis goal now, then I’ll inspect the file schemas and sample values to confirm the 2019 columns and key fields.

  10. update_analysis_brief

    Recorded tool call · completed

  11. get_item

    Recorded tool call · completed

  12. get_item

    Recorded tool call · completed

  13. get_item

    Recorded tool call · completed

  14. describe_processors

    Recorded tool call · completed

  15. get_item

    Recorded tool call · completed

  16. Progress update

    The source files do expose the fields we need: `ISO_A3` on boundaries, `Country Code` in the tables, and a real `2019` numeric column in each indicator. I’m now running one compact full-world trial that joins all three tables, calculates rural share, and applies the bivariate quantile classes so I can inspect the actual known/unknown coverage and class fields.

  17. inspect_artifact

    Recorded tool call · failed

  18. inspect_artifact

    Recorded tool call · failed

  19. get_tool_help

    Recorded tool call · completed

  20. inspect_artifact

    Recorded tool call · failed

  21. get_tool_help

    Recorded tool call · completed

  22. inspect_artifact

    Recorded tool call · failed

  23. get_tool_help

    Recorded tool call · completed

  24. get_tool_help

    Recorded tool call · completed

  25. describe_processors

    Recorded tool call · completed

  26. list_processors

    Recorded tool call · completed

  27. describe_processors

    Recorded tool call · completed

  28. Progress update

    One practical uncertainty remains: the three tables all use the same year column name, so I need to confirm how Blue handles repeated `2019` fields across sequential joins. I’m checking that now so the saved workflow keeps the exact values without a name collision.

  29. list_processors

    Recorded tool call · completed

  30. run_trial

    Recorded tool call · completed

  31. Progress update

    The join question is answered: Blue kept all 251 country features and auto-resolved the repeated year column names as separate fields instead of failing. I’m verifying which renamed field is rural vs total, then I’ll run the complete 2019 join, calculation, and bivariate class test before saving the final map workflow.

  32. get_trial_report

    Recorded tool call · completed

  33. inspect_artifact

    Recorded tool call · completed

  34. inspect_artifact

    Recorded tool call · completed

  35. inspect_artifact

    Recorded tool call · completed

  36. Progress update

    I’ve confirmed the field mapping now: the first join’s `2019_x` values are rural population, and `2019_y` is total population from the second join. Next I’m checking the workflow schema for the saved production run, then I’ll test the full calculation and bivariate class output before adding the final layer to your map.

  37. get_tool_help

    Recorded tool call · completed

  38. get_tool_help

    Recorded tool call · completed

  39. get_tool_help

    Recorded tool call · completed

  40. get_tool_help

    Recorded tool call · completed

  41. get_tool_help

    Recorded tool call · completed

  42. get_tool_help

    Recorded tool call · completed

  43. assess_trial

    Recorded tool call · completed

  44. Progress update

    I’m running the complete world test now. This one covers the third join, the rural-share calculation, and the bivariate classifier itself so I can verify the final fields and unknown handling before I save the production workflow.

  45. run_trial

    Recorded tool call · completed

  46. Progress update

    The full test completed successfully. I’m checking the final class counts and preview from the tested output now so the saved workflow and final answer use exact known and unknown totals.

  47. get_trial_report

    Recorded tool call · completed

  48. inspect_artifact

    Recorded tool call · completed

  49. inspect_artifact

    Recorded tool call · completed

  50. inspect_artifact

    Recorded tool call · completed

  51. Progress update

    I’ve got the tested final fields and units. I’m pulling the exact zero-class and nonzero-class counts now so the saved workflow and final answer use the real known/unknown coverage from the finished bivariate output.

  52. inspect_artifact

    Recorded tool call · completed

  53. inspect_artifact

    Recorded tool call · completed

  54. inspect_artifact

    Recorded tool call · completed

  55. Progress update

    The method is now tested end to end. It preserves all 251 supplied boundary features, gives 196 rows with both measurements known and 55 neutral unknowns, and keeps the numeric X and Y fields on all rows. I’m saving that tested method as the production workflow now, then I’ll run it and bind the final layer to your map.

  56. assess_trial

    Recorded tool call · completed

  57. update_analysis_brief

    Recorded tool call · completed

  58. Progress update

    I have the tested method and exact counts. I’m saving that exact computation now so the final run uses the checked joins, formula, and 3x3 quantile classing without changing scope.

  59. propose_workflow

    Recorded tool call · completed

  60. Progress update

    The production workflow is saved and validated. I’m running that exact tested workflow now, then I’ll inspect the delivered artifact and confirm the map layer ID from the actual result.

  61. execute_workflow

    Recorded tool call · completed

  62. Progress update

    The saved run finished. I’m inspecting the actual delivered output now, then I’ll bind that verified final layer to your map and record the final artifact and layer ID.

  63. inspect_workflow_results

    Recorded tool call · completed

  64. Progress update

    I already have the verified map layer binding from the finished run. I’m checking a few final output rows on that delivered artifact so the accepted result records the exact coverage and field behavior from the actual map layer, not just the private trial.

  65. inspect_artifact

    Recorded tool call · completed

  66. inspect_artifact

    Recorded tool call · completed

  67. inspect_artifact

    Recorded tool call · completed

  68. inspect_artifact

    Recorded tool call · completed

  69. Progress update

    I’m re-reading the finished map output itself now. The delivered artifact matches the tested method, but I want the current final inspection receipts before I record the accepted result.

  70. inspect_artifact

    Recorded tool call · completed

  71. inspect_artifact

    Recorded tool call · completed

  72. inspect_artifact

    Recorded tool call · completed

  73. inspect_artifact

    Recorded tool call · completed

  74. Progress update

    I’m checking the final class counts and the classifier summary from the delivered map output. That lets me verify the neutral unknown class and the legend ranges from the actual final artifact before I accept it.

  75. inspect_artifact

    Recorded tool call · completed

  76. inspect_artifact

    Recorded tool call · completed

  77. inspect_artifact

    Recorded tool call · completed

  78. assess_result

    Recorded tool call · failed

  79. Progress update

    I have the final evidence. Blue rejected my first final assessment because the condition text has to match your accepted result checks exactly, so I’m resubmitting the same evidence against the exact saved conditions now.

  80. assess_result

    Recorded tool call · completed

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