Research/Terra/ 663841
Task evidence / bivariate-map

Make a map to explore the relationship between forest area (% of land area) and annual freshwater withdrawals (% of internal resources) in 2015 globally.

PassComputational taskUnpublished draft
Download evidence JSON ↓

The question

663841
Make a map to explore the relationship between forest area (% of land area) and annual freshwater withdrawals (% of internal resources) in 2015 globally.
Exact submitted task and declared adaptations
Make a map to explore the relationship between forest area (% of land area) and annual freshwater withdrawals (% of internal resources) in 2015 globally.

Task conventions: Use the frozen country boundaries and the 2015 column of every supplied indicator. X is forest_percent (% of land area); Y is water_withdrawal (% of internal resources). 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 AG.LND.FRST.ZS",
    "edition": "Frozen GeoBenchX World Development Indicators; use 2021",
    "units": {
      "2021": "% of land area"
    },
    "collectionId": "blue-generated--[resource id withheld]",
    "itemId": "[resource id withheld]",
    "assetKey": "data"
  },
  {
    "name": "Freshwater withdrawals as percent of internal resources",
    "edition": "Frozen World Development Indicators; use 2021",
    "units": {
      "2021": "percent of internal freshwater resources"
    },
    "collectionId": "blue-generated--[resource id withheld]",
    "itemId": "[resource id withheld]",
    "assetKey": "data"
  }
]

Outcome & verification

Independent artifact and delivery checks passed.

Duration
20m 22s
Recorded Blue tool calls
56
Total tokens
2,514,681
Model cost
Not recorded
Application revision
d50497ea9084f29c91b05406688f87035df98d8a
Release
terra-geobenchx-v1

Recorded final answer

Done — the 2015 global bivariate layer is on your map. It uses the supplied frozen World Bank country boundaries plus the supplied 2015 forest area (% of land area) and annual freshwater withdrawals (% of internal freshwater resources) indicators. All 251 country features were kept; 179 countries have both values and 72 stay neutral because one or both 2015 values are unknown. The map shows a mixed global pattern rather than one clear trend. Use it to explore association, not causation. ### Limitations - The map shows co-occurrence only, not causality. - Countries missing either 2015 value remain neutral class 0. - Countries missing either supplied 2015 value remain neutral class 0. - The left joins preserve all boundary rows, but some joined indicator values remain unknown. - Use the supplied 2015 column only; missing values stay unknown. - Rows missing either measurement are neutral class 0. - The map supports relationship exploration, not causal inference.

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
179
Reported count
179
Computation verified
Yes
Answer fulfilled
Yes
Full grading and rubric record
{
  "expectedCount": 179,
  "actualCount": 179,
  "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 exact left joins from the supplied boundaries to the two supplied 2015 indicator tables preserve every boundary row and produce the expected bivariate class fields for the final global map?

assess_trial · completed · assessment: insufficient

The trial answered the join part of the question. The joined global output preserved all 251 boundary features and benchmark_row_id values, and the joined numeric fields are named 2015_x for forest and 2015_y for water withdrawals. The final classifier step failed only because it referenced a non-existent field name (2015) instead of the actual joined field name.

run_trial · completed

Does the corrected bivariate classifier produce the required global joint classes from the exact joined 2015 forest and water fields, keeping unknown countries in class 0 and preserving every original country row?

assess_trial · completed · assessment: sufficient

The corrected classifier produced a map-ready global output from the exact joined 2015 fields. The output preserved all 251 country features and benchmark_row_id values, retained the numeric fields 2015_x and 2015_y with the stated units, and added joint_class, joint_class_x and joint_class_y fields. joint_class spans 0 through 9, so the result includes a neutral unknown class 0 and nonzero bivariate classes for countries where both values are known.

assess_result · completed · assessment: sufficient

The global 2015 bivariate choropleth is published and on the map. It keeps all 251 frozen country features, classifies 179 countries where both supplied 2015 measures are known, and leaves 72 countries in neutral class 0 because one or both supplied 2015 values are unknown. Forest area is shown as % of land area and annual freshwater withdrawals as % of internal freshwater resources. The resulting pattern is mixed rather than a single clear global trend, so it supports relationship exploration but not causal claims.

Recorded workflow

  1. 01

    Bivariate classes

    Recorded vector-bivariate-classify · succeeded

  2. 02

    Frozen country boundaries

    Recorded source · succeeded

  3. 03

    Global forest and freshwater-withdrawal bivariate map (2015)

    Recorded output · succeeded

  4. 04

    Forest area table

    Recorded source · succeeded

  5. 05

    Join forest 2015

    Recorded table-attribute-join · succeeded

  6. 06

    Join water-withdrawal 2015

    Recorded table-attribute-join · succeeded

  7. 07

    Freshwater withdrawals table

    Recorded source · succeeded

  8. 08

    Bivariate classes

    Recorded vector-bivariate-classify · succeeded

  9. 09

    Joined global country indicators

    Recorded source · succeeded

  10. 10

    Bivariate classes

    Recorded output · succeeded

  11. 11

    Bivariate classes

    Recorded vector-bivariate-classify · failed

  12. 12

    Frozen country boundaries

    Recorded source · succeeded

  13. 13

    Forest 2015 table

    Recorded source · succeeded

  14. 14

    Join forest 2015

    Recorded table-attribute-join · succeeded

  15. 15

    Join water-withdrawal 2015

    Recorded table-attribute-join · succeeded

  16. 16

    Join water-withdrawal 2015

    Recorded output · succeeded

  17. 17

    Bivariate classes

    Recorded output · skipped

  18. 18

    Water withdrawal 2015 table

    Recorded source · 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 supplied boundary and indicator files first so I can use the exact 2015 fields and preserve every country row, including unknowns and duplicates. After that I’ll test the join and classing on a small private run before saving the full map workflow.

  2. get_collection

    Recorded tool call · completed

  3. get_collection

    Recorded tool call · completed

  4. get_analysis

    Recorded tool call · completed

  5. get_collection

    Recorded tool call · completed

  6. Progress update

    I found the execution tools. Next I’m pulling the supplied file schemas and processor contracts so I can build the join and quantile-class map without guessing field names or parameters.

  7. get_item

    Recorded tool call · completed

  8. get_item

    Recorded tool call · completed

  9. get_item

    Recorded tool call · completed

  10. Progress update

    I confirmed the supplied files include the exact `2015` columns and the join keys the task names (`ISO_A3` and `Country Code`). Now I’m looking for the safest processor chain to keep every boundary row, carry the original numeric values through, and compute the joint class exactly as requested.

  11. list_processors

    Recorded tool call · completed

  12. list_processors

    Recorded tool call · completed

  13. list_processors

    Recorded tool call · completed

  14. list_processors

    Recorded tool call · completed

  15. describe_processors

    Recorded tool call · completed

  16. describe_processors

    Recorded tool call · completed

  17. update_analysis_brief

    Recorded tool call · completed

  18. Progress update

    I have the join and bivariate-classifier contracts. The remaining uncertainty is practical: what field names the joined output actually produces, whether the left joins preserve all 251 boundary rows, and whether the classifier emits the expected joint class and legend. Because this dataset is small, I’m testing the real global chain now in a private trial instead of a toy subset.

  19. get_tool_help

    Recorded tool call · completed

  20. get_tool_help

    Recorded tool call · completed

  21. get_tool_help

    Recorded tool call · completed

  22. get_tool_help

    Recorded tool call · completed

  23. run_trial

    Recorded tool call · completed

  24. Progress update

    The join itself likely worked, but the classifier pointed at the wrong field names. I’m inspecting the trial output now, then I’ll correct the class step to use the actual joined columns and keep going with the full map.

  25. Progress update

    I confirmed the key point from the trial: the joins kept all 251 country features, and the actual joined numeric fields are `2015_x` and `2015_y`. I’m fixing the class step to use those exact fields, then I’ll rerun the tested global method and inspect the final layer counts and legend.

  26. get_tool_help

    Recorded tool call · completed

  27. get_tool_help

    Recorded tool call · completed

  28. get_tool_help

    Recorded tool call · completed

  29. get_tool_help

    Recorded tool call · completed

  30. get_tool_help

    Recorded tool call · completed

  31. get_tool_help

    Recorded tool call · completed

  32. get_tool_help

    Recorded tool call · completed

  33. get_tool_help

    Recorded tool call · completed

  34. get_trial_report

    Recorded tool call · completed

  35. assess_trial

    Recorded tool call · completed

  36. Progress update

    I fixed the method. Now I’m rerunning only the missing step on the exact joined global artifact so I can verify the class field and map-ready legend before I save the final workflow.

  37. run_trial

    Recorded tool call · completed

  38. Progress update

    The corrected classifier succeeded. I’m doing the last checks now: exact counts for known versus unknown countries, a quick look at the class values in the finished vector, and then I’ll save and run the production workflow from the original supplied sources so the map layer is based on a final retained artifact, not a private trial.

  39. get_trial_report

    Recorded tool call · completed

  40. inspect_artifact

    Recorded tool call · completed

  41. inspect_artifact

    Recorded tool call · completed

  42. Progress update

    I’ve already confirmed the corrected global class output in private. I’m now turning that into the retained final workflow from the supplied source files, then I’ll add the finished bivariate layer to the map and record the final answer object against the verified artifact.

  43. Progress update

    I’ve finished the private method check. Next I’m saving the final workflow from the original boundary and indicator files, then I’ll inspect the published output and add that verified layer to the map so the final answer points to the retained artifact.

  44. get_tool_help

    Recorded tool call · completed

  45. get_tool_help

    Recorded tool call · completed

  46. get_analysis

    Recorded tool call · completed

  47. inspect_artifact

    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 have enough evidence to save the final method now. I’m packaging the tested source → join → bivariate-class workflow with the user’s exact join keys, year, and class rules so the retained final artifact comes from the original supplied files.

  52. propose_workflow

    Recorded tool call · completed

  53. Progress update

    The corrected trial is good enough to answer the remaining method question, so I’m recording that now. Then I’ll clear the earlier field-name finding, run the saved final workflow, and bind the verified output to the map.

  54. assess_trial

    Recorded tool call · completed

  55. assess_plan

    Recorded tool call · failed

  56. assess_plan

    Recorded tool call · completed

  57. Progress update

    I’ve resolved the earlier method issue. Blue rejected the plan-assessment call only because I cited an insufficient exploratory trial alongside the sufficient correction trial, so I’m correcting that reference and continuing.

  58. execute_workflow

    Recorded tool call · completed

  59. Progress update

    The final workflow is now supported by the corrected trial evidence. I’m executing that saved source-based workflow now, then I’ll inspect the retained output, add the verified layer, and finish the answer object from the final artifact.

  60. Progress update

    The saved workflow has finished successfully. I’m inspecting the final retained output now so I can pull the exact artifact selection, confirm the class counts on the published result, and then add that verified layer to your map.

  61. inspect_workflow_results

    Recorded tool call · completed

  62. list_map_layers

    Recorded tool call · completed

  63. Progress update

    I have the final published artifact and the live map layer ID. I’m doing the last bounded checks now: exact counts for neutral versus classified countries, and the processor summary that carries the bivariate class ranges and units.

  64. inspect_artifact

    Recorded tool call · completed

  65. inspect_artifact

    Recorded tool call · completed

  66. inspect_artifact

    Recorded tool call · completed

  67. inspect_artifact

    Recorded tool call · completed

  68. Progress update

    I have the published counts and class breaks from the final summary. I’m pulling one final preview of the published vector now so the accepted result cites the retained artifact itself, including the benchmark IDs and the neutral class behavior.

  69. inspect_artifact

    Recorded tool call · completed

  70. inspect_artifact

    Recorded tool call · completed

  71. inspect_artifact

    Recorded tool call · completed

  72. Progress update

    I’ve got the published summary with 179 paired countries and 72 neutral unknowns. I’m just collecting the final preview receipt from the published vector so the accepted result cites the retained artifact itself, then I’ll record the final answer object.

  73. inspect_artifact

    Recorded tool call · completed

  74. assess_result

    Recorded tool call · completed

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