Map the relationship between GDP per capita and electric power consumption per capita globally.
The question
301626Map the relationship between GDP per capita and electric power consumption per capita globally.
Exact submitted task and declared adaptations
Map the relationship between GDP per capita and electric power consumption per capita globally.
Task conventions: Use the frozen country boundaries and the 2014 column of every supplied indicator. X is gdp (current USD per capita); Y is electricity (kWh per capita). 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": "GDP per capita",
"edition": "Frozen WDI; current US dollars by year",
"units": {
"2023": "current USD per capita",
"2020": "current USD per capita",
"2022": "current USD per capita"
},
"collectionId": "blue-generated--[resource id withheld]",
"itemId": "[resource id withheld]",
"assetKey": "data"
},
{
"name": "Electric power consumption per capita",
"edition": "Frozen WDI; use 2014",
"units": {
"2014": "kWh per capita"
},
"collectionId": "blue-generated--[resource id withheld]",
"itemId": "[resource id withheld]",
"assetKey": "data"
}
]Outcome & verification
Independent artifact and delivery checks passed.
- Duration
- 24m 5s
- Recorded Blue tool calls
- 57
- Total tokens
- 2,696,910
- Model cost
- Not recorded
- Application revision
- d50497ea9084f29c91b05406688f87035df98d8a
- Release
- terra-geobenchx-v1
Recorded final answer
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
- 140
- Reported count
- 140
- Computation verified
- Yes
- Answer fulfilled
- Yes
Full grading and rubric record
{
"expectedCount": 140,
"actualCount": 140,
"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 the supplied boundary and indicator files expose the exact ISO_A3, Country Code, benchmark_row_id, and 2014 value fields needed for the required joins and bivariate map?
assess_trial · completed · assessment: sufficient
The private source trial answered the field-selection question. The boundary file exposes ISO_A3 and benchmark_row_id. Both indicator tables expose Country Code and a numeric 2014 field. The files are sufficient to build the required exact-key joins without guessing years or IDs.
run_trial · completed
When joining both supplied 2014 tables to the country boundaries by ISO_A3 = Country Code, how are the copied year fields named and are all original boundary rows preserved?
assess_trial · completed · assessment: sufficient
The full-scope join trial answered the join-behavior question. A left join preserved all 251 supplied country boundary rows and benchmark_row_id values. After both joins, the copied indicator fields are available as 2014_x for GDP and 2014_y for electricity, so the classifier can reference them directly. The join receipts also report 217 matched input features and 34 unmatched input features under the exact ISO_A3 = Country Code rule.
run_trial · completed
Does the bivariate classifier produce the required joint-class fields, neutral missing class, known-row count, and legend ranges with the requested 2014 units when applied to the exact joined country artifact?
assess_trial · completed · assessment: sufficient
The private bivariate classification trial answered the method question. The output preserved all 251 country features and benchmark_row_id values. It added the requested class fields joint_class, joint_class_x, and joint_class_y while retaining 2014_x and 2014_y as numeric GDP and electricity values. The summary reports quantile breaks over paired known rows only, uses the requested units, sets class 0 for missing pairs, and reports 140 paired known rows with 111 rows missing either measurement.
assess_result · failed · requested assessment: sufficient
The final global 2014 bivariate country layer is delivered and on the map. It preserves all 251 supplied country features and benchmark_row_id values, retains the joined numeric GDP and electricity values, and applies the required three-by-three quantile bivariate classes with neutral class 0 for missing pairs. Across countries with both measurements known, the pattern shows a broad positive association: many higher-GDP countries fall in higher electricity-use classes and many lower-GDP countries fall in lower classes, with clear exceptions. This is an association map, not evidence of causation.
assess_result · completed · assessment: sufficient
The final global 2014 bivariate country layer is delivered and on the map. It preserves all 251 supplied country features and benchmark_row_id values, retains the joined numeric GDP and electricity values, and applies the required three-by-three quantile bivariate classes with neutral class 0 for missing pairs. Across countries with both measurements known, the pattern shows a broad positive association: many higher-GDP countries fall in higher electricity-use classes and many lower-GDP countries fall in lower classes, with clear exceptions. This is an association map, not evidence of causation.
Recorded workflow
- 01
Country boundaries
Recorded source · succeeded
- 02
Bivariate GDP and electricity classes
Recorded vector-bivariate-classify · succeeded
- 03
Global GDP and electricity joint classes (2014)
Recorded output · succeeded
- 04
Electricity table
Recorded source · succeeded
- 05
GDP table
Recorded source · succeeded
- 06
Join electricity 2014
Recorded table-attribute-join · succeeded
- 07
Join GDP 2014
Recorded table-attribute-join · succeeded
- 08
Bivariate GDP and electricity classes
Recorded vector-bivariate-classify · succeeded
- 09
Joined country indicators
Recorded source · succeeded
- 10
Bivariate GDP and electricity classes
Recorded output · succeeded
- 11
Bivariate GDP and electricity classes
Recorded output · succeeded
- 12
Country boundaries
Recorded source · succeeded
- 13
Electricity table
Recorded source · succeeded
- 14
GDP table
Recorded source · succeeded
- 15
Join electricity 2014
Recorded table-attribute-join · succeeded
- 16
Join GDP 2014
Recorded table-attribute-join · succeeded
- 17
Join GDP 2014
Recorded output · succeeded
- 18
Join electricity 2014
Recorded output · succeeded
- 19
Boundaries source
Recorded source · succeeded
- 20
Electricity source
Recorded source · succeeded
- 21
GDP source
Recorded source · succeeded
- 22
Boundaries source
Recorded output · succeeded
- 23
GDP source
Recorded output · succeeded
- 24
Electricity source
Recorded output · succeeded
Activity
Recorded actionsRecorded 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.
Progress update
I’ll inspect the saved analysis context, the supplied datasets, and the available workflow tools first. Then I’ll test the join/classing method privately before saving the production workflow.
Progress update
I’ve started by checking the retained analysis state and the three supplied inputs. Next I’ll confirm the exact boundary and indicator fields, then I’ll run a small private test of the join and joint-class rules before I save the full global workflow.
get_analysis
Recorded tool call · completed
get_map_context
Recorded tool call · completed
get_collection
Recorded tool call · completed
get_collection
Recorded tool call · completed
get_collection
Recorded tool call · completed
get_collection
Recorded tool call · completed
get_collection
Recorded tool call · completed
get_collection
Recorded tool call · completed
Progress update
The supplied inputs are single immutable files from Blue. I have the global boundary extent and the source descriptions. Next I’m sampling each file to confirm the exact column names before I build the join and classing test.
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
list_processors
Recorded tool call · completed
list_processors
Recorded tool call · completed
list_processors
Recorded tool call · completed
describe_processors
Recorded tool call · completed
describe_processors
Recorded tool call · completed
Progress update
I found a dedicated bivariate classifier that already matches the user’s class rule: it computes classes only where both values are known, collapses tied quantile breaks, puts equality into the upper class, and assigns class 0 to missing pairs. I’m checking the exact join and classifier schemas now.
Progress update
The processor schemas look right: a left attribute join will preserve every original country row, and the classifier already implements the requested joint-class formula and missing-value rule. I still need the exact field names from the supplied files, so I’m inspecting the source items directly now.
inspect_artifact
Recorded tool call · failed
inspect_artifact
Recorded tool call · failed
inspect_artifact
Recorded tool call · failed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
Progress update
I’ve confirmed there’s no saved goal yet, so I’m recording the analysis brief now. That gives me a stable result target for the private trial and the final assessment.
update_analysis_brief
Recorded tool call · completed
Progress update
I’m running a private source-only trial now. I expect it to expose the exact join keys and 2014 value columns from each file so I can build the full workflow without guessing field names.
run_trial
Recorded tool call · completed
Progress update
The source-only trial succeeded. I confirmed the required fields are present: the boundary file has `ISO_A3` and `benchmark_row_id`, and both indicator tables have `Country Code` plus a numeric `2014` column. I’m recording that finding, then I’ll save the full global join-and-class workflow.
get_trial_report
Recorded tool call · completed
assess_trial
Recorded tool call · completed
Progress update
I confirmed the exact source fields. Before I publish anything, I’m checking one more method detail: how to keep both tables’ `2014` values without a name collision in the joined vector. Then I’ll test the full global computation privately.
list_processors
Recorded tool call · completed
list_processors
Recorded tool call · completed
list_processors
Recorded tool call · completed
describe_processors
Recorded tool call · completed
Progress update
I’m testing the two joins exactly as requested on the full global scope. What I need to observe is whether the second copied `2014` field is preserved with a distinct name or whether I need an extra rename step before classification.
run_trial
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_trial_report
Recorded tool call · completed
assess_trial
Recorded tool call · completed
Progress update
I’ve confirmed the second join keeps both copied year fields as `2014_x` and `2014_y`, while preserving all 251 original boundary rows and `benchmark_row_id`. Next I’m running the classifier privately on that exact joined output to verify the class fields, counts, and legend before I save the final workflow.
Progress update
I’ve verified the exact joined fields on the full global data. Now I’m testing the bivariate classifier itself on that exact joined artifact so I can confirm the final class field names, the count of rows with both values known, and the legend ranges with units before I publish the map layer.
run_trial
Recorded tool call · completed
Progress update
The private classifier test finished successfully. It produced the exact fields I need for delivery: `joint_class`, `joint_class_x`, `joint_class_y`, retained `2014_x` and `2014_y`, and a summary with 140 paired known rows and 111 unknown rows. I’m saving the production workflow now from this tested method, then I’ll execute it and bind the final layer to the map.
get_trial_report
Recorded tool call · completed
assess_trial
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
Progress update
I’ve finished the method development. The only remaining step is delivery: run the saved production workflow, inspect the published output, and add the verified layer to the map. I’m refreshing the brief so it no longer says the fields are unconfirmed.
update_analysis_brief
Recorded tool call · completed
Progress update
I’ve saved intermediate checks. Now I’m validating the production workflow that reruns the exact joins from the supplied immutable inputs and publishes the final bivariate country layer.
propose_workflow
Recorded tool call · failed
Progress update
The workflow is scientifically fine; the compiler just rejected my evidence binding because I pointed at the classifier’s literal `xField` and `yField` settings instead of its dataset input. I’m correcting that reference now and resubmitting the same method.
propose_workflow
Recorded tool call · completed
execute_workflow
Recorded tool call · completed
Progress update
The production workflow is saved and ready. It reruns the exact full-global joins and the tested classifier settings from the supplied immutable inputs, so I’m executing it now and will inspect the published output before adding the map layer.
inspect_workflow_results
Recorded tool call · completed
list_map_layers
Recorded tool call · completed
Progress update
The workflow run finished in the background. I’m inspecting the published output now to confirm the final artifact identity, then I’ll add that verified layer to the map and record the requested answer.
Progress update
The final layer is already bound on the map. I’m reading the exact workflow output now so I can cite the published artifact identity, inspect the final values once, and record the accepted answer object.
Progress update
I’m reading the final run directly now: the published vector output and its summary. That will give me the artifact reference, the final item identity, and the same counts and legend ranges on the production run before I lock the result.
inspect_workflow_results
Recorded tool call · completed
inspect_artifact
Recorded tool call · completed
inspect_artifact
Recorded tool call · completed
Progress update
I have the final published vector item, its map layer ID, and the production summary with the exact class breaks and counts. I’m checking one last tool detail now so I can record the accepted result with the right artifact references on the first try.
inspect_artifact
Recorded tool call · completed
get_tool_help
Recorded tool call · completed
Progress update
I’ve finished the final checks. The production output keeps all 251 supplied country rows, the map layer is live, and the published summary confirms 140 rows with both 2014 measurements known and 111 neutral unknown rows. I’m recording the accepted result now with the selected final vector artifact and layer ID.
assess_result
Recorded tool call · failed
Progress update
The final check wants a preview on the delivered map output itself, not just the upstream classifier node. I’m doing that now, then I’ll record the result again with the same verified layer and item selection.
inspect_artifact
Recorded tool call · completed
Progress update
The preview on the delivered map output is ready now. I’m recording the accepted result with that final preview receipt and the production summary receipt, then I’ll give you the short plain-language takeaway.
inspect_artifact
Recorded tool call · completed
assess_result
Recorded tool call · completed