{"id":"W6275081","doi":"10.1023/a:1015720518936","title":"Income segregation, income inequality and mortality in North American metropolitan areas","year":2001,"lang":"en","type":"article","venue":"GeoJournal","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"Statistics Canada; Health Sciences Centre; McGill University","funders":"","keywords":"Metropolitan area; Demographic economics; Economic inequality; Inequality; Socioeconomic status; Health equity; Population; Neighbourhood (mathematics); Geography; Population health; Index of dissimilarity; Socioeconomics; Demography; Economic growth; Economics; Sociology; Health care","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001609585,0.000107919,0.0002834307,0.0001369029,0.0005082366,0.00009653172,0.0001700977,0.00004389888,0.000142229],"category_scores_gemma":[0.0004463237,0.00009921739,0.00004625027,0.0007930296,0.0003417699,0.0003017453,0.00004897177,0.0002789275,0.000008378897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004807773,"about_ca_system_score_gemma":0.0002350562,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1652307,"about_ca_topic_score_gemma":0.4479979,"domain_scores_codex":[0.9979607,0.0004375885,0.0004360148,0.0001764735,0.0004254181,0.0005638214],"domain_scores_gemma":[0.9990225,0.0001541785,0.0001847324,0.0001445221,0.00008778799,0.0004063169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009502185,0.00002315879,0.9884053,0.00001503052,0.000008107097,0.00002112425,0.001398881,0.000003281586,6.796112e-8,0.007517155,0.00001833976,0.002579982],"study_design_scores_gemma":[0.000227682,0.0000199498,0.9895954,0.00001601036,0.000006030459,0.000006347248,0.004251418,0.00002214529,3.237228e-7,0.003479672,0.002255934,0.0001190525],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925126,0.0001100996,0.00005046165,0.003544641,0.0001667726,0.0001179912,0.00001295874,0.00002923269,0.003455174],"genre_scores_gemma":[0.9978898,0.0006285066,0.0001660084,0.0009262863,0.0002448482,0.00000547415,0.000003301094,0.000006650122,0.0001291535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2827671,"threshold_uncertainty_score":0.840328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03627911362902702,"score_gpt":0.3677894490489543,"score_spread":0.3315103354199273,"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."}}