Research/Terra/ 734213
Task evidence / country-choropleth

Which Eastern African countries had the highest net migration rates in 2019?

PassComputational taskUnpublished draft
Download evidence JSON ↓

The question

734213
Which Eastern African countries had the highest net migration rates in 2019?
Exact submitted task and declared adaptations
Which Eastern African countries had the highest net migration rates in 2019?

Task conventions: Use the frozen country polygons and World Bank 2019 net migration in people and 2019 total population 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 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 SUBREGION in ["Eastern Africa"]; 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.Define annual net migration rate as 1000 times net migration divided by total population, in net migrants per 1000 people. A missing numerator, missing denominator or nonpositive population is unknown. Identify the three highest known rates in descending order; break exact ties by NAME_EN alphabetically. These are rates, not absolute migrant counts.



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. Include top_countries as the three original NAME_EN country names in that ranking order in the final JSON.

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": "Net migration, World Development Indicators",
    "edition": "2024-11-13 snapshot; use 2023",
    "units": {
      "2023": "people (net migration)"
    },
    "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"
  }
]

Outcome & verification

Independent artifact and delivery checks passed.

Duration
23m 6s
Recorded Blue tool calls
71
Total tokens
3,280,479
Model cost
Not recorded
Application revision
d50497ea9084f29c91b05406688f87035df98d8a
Release
terra-geobenchx-v1

Recorded final answer

For the fixed 2019 benchmark, the highest net migration rates in Eastern Africa were Seychelles (3.12), Somalia (3.01), and Uganda (2.50) net migrants per 1,000 people. I added the choropleth layer to your map. It uses five quantile classes across the 18 retained benchmark country rows, with a neutral No data legend category available but unused here because all 18 rows had known values. Limits: this uses the supplied GeoBenchX benchmark geography and frozen 2019 World Bank tables, so it is not current-membership or live data. Sources: Blue-hosted World Bank country boundaries benchmark, World Development Indicators net migration snapshot, and WDI total population snapshot, retrieved 2026-10-07 UTC. ### Limitations - Uses the supplied GeoBenchX benchmark boundary edition for Eastern Africa membership, not a current-membership definition. - Uses the supplied frozen 2019 World Bank inputs rather than newer revisions or live replacements. - The five choropleth classes are quantiles calculated only within the retained Eastern Africa benchmark country rows, so class numbers are relative to this geography. - This result uses the supplied GeoBenchX benchmark boundary edition for Eastern Africa membership, not a current-membership definition. - This result uses the supplied frozen 2019 World Bank inputs rather than newer revisions or live replacements. - The five choropleth classes are quantiles calculated only within these 18 retained benchmark country rows, so class numbers are relative to this geography. - Uses the supplied benchmark geography edition for Eastern Africa membership. - Quantile classes are relative only within the retained Eastern Africa benchmark country rows.

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
18
Reported count
18
Computation verified
Yes
Answer fulfilled
Yes
Full grading and rubric record
{
  "expectedCount": 18,
  "actualCount": 18,
  "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 the supplied Eastern Africa benchmark country rows be joined to the frozen 2019 World Bank net migration and population tables, while keeping all original rows and producing the required per-1000 rate and quantile classes with unknowns preserved?

assess_trial · completed · assessment: sufficient

The tested computation retained all 18 benchmark Eastern Africa country rows, matched all 18 exact ISO_A3 to Country Code joins for both supplied 2019 World Bank tables, calculated 18 known 2019 net migration rates, and produced a five-class quantile choropleth with class counts 4, 3, 4, 3, and 4. The classified artifact also exposes the published legend metadata, including the neutral No data category value 0, numeric breakpoints, units, and the boundary rule that break values enter the upper class.

assess_result · failed · requested assessment: sufficient

I mapped the 2019 annual net migration rate for all 18 benchmark Eastern Africa country rows and added the classified country layer to the map. The highest known rates are Seychelles, Somalia, and Uganda. All retained rows had known 2019 inputs after the exact code joins, so the neutral No data category is available in the legend but unused in this run.

assess_result · failed · requested assessment: sufficient

I mapped the 2019 annual net migration rate for all 18 benchmark Eastern Africa country rows and added the classified country layer to the map. The highest known rates are Seychelles, Somalia, and Uganda. All retained rows had known 2019 inputs after the exact code joins, so the neutral No data category is available in the legend but unused in this run.

assess_result · completed · assessment: sufficient

I completed the formal final workflow, added its classified Eastern Africa country layer to the map, and verified the final artifact. The highest 2019 net migration rates are Seychelles, Somalia, and Uganda. All 18 retained benchmark country rows had known 2019 values after the exact code joins, so the No data legend category is available but unused in this run.

Recorded workflow

  1. 01

    Frozen country boundaries

    Recorded source · succeeded

  2. 02

    Classify rate

    Recorded vector-classify · succeeded

  3. 03

    Combine migration and positive population

    Recorded vector-spatial-join · succeeded

  4. 04

    Eastern Africa net migration rate (2019)

    Recorded output · succeeded

  5. 05

    Keep Eastern Africa rows

    Recorded vector-filter · succeeded

  6. 06

    Name migration field

    Recorded vector-field-calculate · succeeded

  7. 07

    Join migration table

    Recorded table-attribute-join · succeeded

  8. 08

    Frozen net migration table

    Recorded source · succeeded

  9. 09

    Name population field

    Recorded vector-field-calculate · succeeded

  10. 10

    Join population table

    Recorded table-attribute-join · succeeded

  11. 11

    Keep positive populations

    Recorded vector-filter · succeeded

  12. 12

    Frozen population table

    Recorded source · succeeded

  13. 13

    Calculate rate

    Recorded vector-field-calculate · succeeded

  14. 14

    Frozen country boundaries

    Recorded source · succeeded

  15. 15

    Classify rate

    Recorded vector-classify · succeeded

  16. 16

    Combine migration and positive population

    Recorded vector-spatial-join · succeeded

  17. 17

    Keep Eastern Africa rows

    Recorded vector-filter · succeeded

  18. 18

    Name migration field

    Recorded vector-field-calculate · succeeded

  19. 19

    Join migration table

    Recorded table-attribute-join · succeeded

  20. 20

    Frozen net migration table

    Recorded source · succeeded

  21. 21

    Name population field

    Recorded vector-field-calculate · succeeded

  22. 22

    Join population table

    Recorded table-attribute-join · succeeded

  23. 23

    Keep positive populations

    Recorded vector-filter · succeeded

  24. 24

    Frozen population table

    Recorded source · succeeded

  25. 25

    Calculate rate

    Recorded vector-field-calculate · succeeded

  26. 26

    Keep Eastern Africa rows

    Recorded output · succeeded

  27. 27

    Join migration table

    Recorded output · succeeded

  28. 28

    Name population field

    Recorded output · succeeded

  29. 29

    Combine migration and positive population

    Recorded output · succeeded

  30. 30

    Classify rate

    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 supplied country boundaries and frozen World Bank tables first, so I can confirm whether the requested 2019 fields are actually present and then build the country-rate layer with the required unknown category.

  2. get_analysis

    Recorded tool call · completed

  3. get_map_context

    Recorded tool call · completed

  4. Progress update

    I found no saved analysis yet. Next I’m inspecting the three supplied frozen inputs to confirm the exact row fields, the Eastern Africa filter field, and whether the requested 2019 values are present in the frozen World Bank tables.

  5. get_collection

    Recorded tool call · completed

  6. get_collection

    Recorded tool call · completed

  7. get_collection

    Recorded tool call · completed

  8. get_item

    Recorded tool call · completed

  9. get_item

    Recorded tool call · completed

  10. get_item

    Recorded tool call · completed

  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. list_processors

    Recorded tool call · completed

  16. list_processors

    Recorded tool call · completed

  17. list_processors

    Recorded tool call · completed

  18. list_processors

    Recorded tool call · completed

  19. Progress update

    The frozen tables do contain a `2019` column, so I can stay on the requested year. I’m now discovering the exact processors for filtering the Eastern Africa rows, joining by code, calculating the per-1000 rate, and classifying the five quantiles without dropping unknowns.

  20. describe_processors

    Recorded tool call · completed

  21. Progress update

    I have the four key processors. Next I’m reading their exact schemas so I can test one short end-to-end slice: filter Eastern Africa rows, left-join both frozen tables by code, calculate the 2019 rate, and apply the required quantile classification with a separate No data class.

  22. describe_processors

    Recorded tool call · completed

  23. update_analysis_brief

    Recorded tool call · completed

  24. get_tool_help

    Recorded tool call · completed

  25. get_tool_help

    Recorded tool call · completed

  26. get_tool_help

    Recorded tool call · completed

  27. describe_processors

    Recorded tool call · completed

  28. get_tool_help

    Recorded tool call · failed

  29. get_tool_help

    Recorded tool call · failed

  30. Progress update

    I’ve hit one practical uncertainty: both World Bank tables use a column literally named `2019`. I’m checking whether the join tool can safely carry both through or whether I need a small extra preparation step to rename one before the second join.

  31. list_processors

    Recorded tool call · completed

  32. list_processors

    Recorded tool call · completed

  33. list_processors

    Recorded tool call · completed

  34. list_processors

    Recorded tool call · completed

  35. list_processors

    Recorded tool call · completed

  36. list_processors

    Recorded tool call · completed

  37. describe_processors

    Recorded tool call · completed

  38. run_trial

    Recorded tool call · completed

  39. Progress update

    The private test finished while the turn was interrupted. It worked: 18 benchmark Eastern Africa country rows were kept, both 2019 joins matched all 18 exact codes, and the test produced a full rate field and quantile classes. I’m checking the final rows and legend details now, then I’ll turn that tested method into the deliverable layer and answer.

  40. get_trial_report

    Recorded tool call · completed

  41. inspect_artifact

    Recorded tool call · completed

  42. inspect_artifact

    Recorded tool call · completed

  43. inspect_artifact

    Recorded tool call · completed

  44. inspect_artifact

    Recorded tool call · completed

  45. inspect_workflow_results

    Recorded tool call · completed

  46. get_tool_help

    Recorded tool call · completed

  47. list_processor_jobs

    Recorded tool call · completed

  48. list_map_layers

    Recorded tool call · completed

  49. get_tool_help

    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. Progress update

    I’ve confirmed the final country values: all 18 retained Eastern Africa benchmark rows have known 2019 rates, so the unknown category is present as a rule but unused here. I’m now turning the tested computation into the final map result so I can bind the choropleth layer and submit the required answer object.

  54. get_tool_help

    Recorded tool call · completed

  55. get_tool_help

    Recorded tool call · completed

  56. get_tool_help

    Recorded tool call · completed

  57. Progress update

    I have the exact ranking now: Seychelles, Somalia, and Uganda. I’m checking whether the classified output exposes its published legend directly, so the map layer uses the intended quantile styling instead of a plain vector display.

  58. get_tool_help

    Recorded tool call · completed

  59. get_tool_help

    Recorded tool call · completed

  60. get_item

    Recorded tool call · completed

  61. get_collection

    Recorded tool call · completed

  62. Progress update

    I found the exact legend metadata on the classified artifact itself. It carries five quantile breaks, class counts, units, and a No data category value 0, so I can bind the final map layer with the right legend and finish the result.

  63. assess_trial

    Recorded tool call · completed

  64. add_catalog_layer

    Recorded tool call · completed

  65. list_map_layers

    Recorded tool call · completed

  66. get_analysis

    Recorded tool call · completed

  67. Progress update

    The layer is added. I’m doing one quick map check so the final answer points to the live layer ID that’s actually on your map, then I’ll record the accepted result.

  68. assess_result

    Recorded tool call · failed

  69. Progress update

    I hit a validation check: the final assessment wants the acceptance-condition text matched exactly. I’m correcting that wording now and resubmitting the same evidence.

  70. assess_result

    Recorded tool call · failed

  71. Progress update

    I’m saving the tested computation as the formal result workflow now, because Blue won’t accept a private trial artifact as the final selected output. After that I can execute the same method as a final deliverable and bind the final map layer to it.

  72. propose_workflow

    Recorded tool call · completed

  73. Progress update

    The saved draft was blocked because copying the trial also copied its world-sized execution scope, which no longer matched the declared study area. I’m saving the same tested method again with the study scope bound explicitly to the Eastern Africa extent so it can run as the formal final output.

  74. propose_workflow

    Recorded tool call · completed

  75. execute_workflow

    Recorded tool call · completed

  76. Progress update

    I’ve saved the runnable final workflow using the same tested method and the correct Eastern Africa scope. I’m executing that formal final output now so Blue can accept a final deliverable artifact, not just the private test.

  77. Progress update

    The formal final workflow finished in the background. I’m inspecting its delivered output now and then I’ll bind that final artifact to the map so the accepted answer points to the live final layer, not the earlier private test layer.

  78. get_workflow_run

    Recorded tool call · completed

  79. list_map_layers

    Recorded tool call · completed

  80. inspect_workflow_results

    Recorded tool call · completed

  81. Progress update

    I’m inspecting the formal final artifact now. That gives me the final item ID, the verified map layer ID from the workflow, and the exact row values I need for the accepted result object.

  82. inspect_artifact

    Recorded tool call · completed

  83. inspect_artifact

    Recorded tool call · completed

  84. get_item

    Recorded tool call · completed

  85. Progress update

    I’ve got the formal final item and verified final map layer from the completed workflow. I’m fetching the final inspection receipts now, then I can record the accepted result and answer plainly.

  86. inspect_artifact

    Recorded tool call · completed

  87. inspect_artifact

    Recorded tool call · completed

  88. assess_result

    Recorded tool call · completed

|||

© 2026 Exia Labs, Inc.