Show labor force distribution in Arctic Circle countries

Show labor force distribution in Arctic Circle countries

Task conventions: Use the frozen country polygons and World Bank SL.TLF.TOTL.IN 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 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 ["Canada", "Denmark", "Finland", "Iceland", "Norway", "Russian Federation", "Sweden", "United States"]; 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 SL.TLF.TOTL.IN",
    "edition": "Frozen GeoBenchX World Development Indicators; use 2023",
    "units": {
      "2023": "people"
    },
    "collectionId": "blue-generated--[resource id withheld]",
    "itemId": "[resource id withheld]",
    "assetKey": "data"
  }
]

{
  "expectedCount": 7,
  "actualCount": 7,
  "expectedMetrics": null,
  "actualMetrics": null,
  "computationCorrect": true,
  "answerFulfilled": true,
  "adjudication": null,
  "qualification": null,
  "reasons": []
}

Do the supplied benchmark boundaries and frozen World Bank labor-force table support the required exact country-code join and quantile choropleth for the fixed Arctic-country geography without dropping unmatched rows?

The full-scope trial answered the method question. Filtering the supplied benchmark boundaries by the eight listed NAME_EN values retained 8 original country features. The exact left join from ISO_A3 to Country Code preserved all 8 features, matched 7, and left 1 unmatched feature with ISO_A3='-99' as unknown. Quantile classification on the frozen 2023 field kept all 8 rows together, assigned labor_force_class 0 to the unknown row, and produced five numeric classes across the 7 known values.

The delivered map artifact contains 8 fixed benchmark country features and no extras. It preserves original geometry and benchmark_row_id, uses the frozen 2023 labor-force field in people, and keeps all rows together after the exact ISO_A3 to Country Code left join. Seven countries have known values and one Norway feature remains No data because its supplied ISO_A3 is the sentinel -99. The final classifier summary reports five quantile classes over 7 known values, one No data row, and breaks at 2,939,754.6; 4,237,804.2; 15,596,385.0; and 62,348,298.6 people. The final deliverable is added to the map as layer [resource id withheld].