Show rates of deforestation over the last decade

Show rates of deforestation over the last decade.

Task conventions: Use the frozen country polygons and World Bank AG.LND.FRST.ZS 2021 column, in percentage points (2021 minus 2011). 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. 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. Use forest coverage in 2021 minus 2011, in percentage points over the archived decade. Negative values indicate loss; this is not an annual compound percentage or absolute forest area. Both years must exist.



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 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"
  }
]

{
  "expectedCount": 214,
  "actualCount": 214,
  "expectedMetrics": null,
  "actualMetrics": null,
  "computationCorrect": true,
  "answerFulfilled": true,
  "adjudication": null,
  "qualification": null,
  "reasons": []
}

Does the exact full-country join and 2021 minus 2011 classification produce the required country artifact and map-ready choropleth while preserving every supplied boundary row, keeping unmatched or missing countries unknown, and separating No data from zero?

The full-scope trial preserved all 251 supplied boundary features and all benchmark_row_id values through the join, calculation and classification steps. The exact left join on ISO_A3 to Country Code left 34 country features unmatched, including seven ISO_A3='-99' sentinel rows, and those rows stayed unknown with null forest_pp_2021_2011 and class 0 rather than being guessed. The field calculation produced 214 known country values and 37 unknowns, with units recorded as percentage points and a value range from -13.188692499433394 to 7.0370370370369955. The classifier kept five quantile classes for known values, plus class 0 for No data. Inspection of exact zero rows showed 43 countries with forest_pp_2021_2011 = 0.0, all classified as class 4 rather than No data, so genuine zeros stayed numeric. The quantile summary reported breaks at -1.3318747739764603, -0.02263348014239952, 0.0 and 0.45393313408084107, with the processor’s lower-inclusive upper-exclusive class convention matching the required rule that break equality enters the upper class.

Delivered result verified. The final country artifact retains all 251 original boundary features and all benchmark_row_id values. It contains 214 known country values and 37 unknowns. Negative values and genuine zeros are preserved; 43 countries have an exact value of 0.0 and remain in a numeric class, not No data. Seven ISO_A3='-99' sentinel rows remain unmatched and unknown. The choropleth uses five quantile classes with breaks at -1.3318747739764603, -0.02263348014239952, 0.0, and 0.45393313408084107 percentage points, plus a neutral No data class. The delivered map layer is already added to the map. Source snapshots used: Benchmark fixture · World Bank country boundaries and Benchmark fixture · World Bank AG.LND.FRST.ZS, both hosted by Blue from the supplied frozen benchmark collections and retrieved on 2026-10-07; source revision was not provided in the collection metadata. The delivered derived artifact was retrieved at 2026-10-07T08:17:12Z.