{"id":"W6947600173","doi":"10.3886/e192306v4-172821","title":"GEOWEALTH-US: Spatial wealth inequality data for the United States, 1960-2020","year":2025,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inequality; Disadvantage; Economic inequality; Spatial inequality; Imputation (statistics); Distribution (mathematics); National wealth; Population; Survey data collection","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000738202,0.0009135168,0.0007050107,0.002795937,0.000573957,0.001325859,0.001420767,0.0008001606,0.02518441],"category_scores_gemma":[0.003996938,0.0004436008,0.0005331041,0.008656809,0.0003002801,0.00091098,0.001443818,0.001091046,0.02528394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00146316,"about_ca_system_score_gemma":0.001947004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1347991,"about_ca_topic_score_gemma":0.1416526,"domain_scores_codex":[0.9994301,0.00007187414,0.000112324,0.000117203,0.0001740586,0.00009431312],"domain_scores_gemma":[0.9986271,0.0001668759,0.0002704077,0.0002196947,0.0005788404,0.0001372553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002883314,0.0000134486,0.0032347,0.0001514671,0.00001522934,0.00001999807,0.00003365625,0.0001861585,0.00003180945,0.0005425073,0.9933466,0.002395712],"study_design_scores_gemma":[0.0001606154,0.00001975419,0.05418622,0.0003756492,0.00002539567,0.00008199981,0.000298068,0.0007319759,0.0002961435,0.001091984,0.9426896,0.00004273653],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003490543,0.0000345658,0.0000352372,0.00008081598,0.00001431431,0.000006284014,0.9988261,0.00007010923,0.0005834451],"genre_scores_gemma":[0.001179578,0.00005298329,0.0001729237,0.00003885348,0.000007966573,0.00005220306,0.9978521,0.0000178415,0.0006255887],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1347991,"threshold_uncertainty_score":0.2680289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1023012044063629,"score_gpt":0.3809089988874051,"score_spread":0.2786077944810422,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}