{"id":"W4402078561","doi":"10.1038/s41597-024-03771-6","title":"A synthetic vulnerable population dataset for fine scale geographical equity analysis and urban planning","year":2024,"lang":"en","type":"article","venue":"Scientific Data","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Transport Canada","funders":"","keywords":"Comparability; Equity (law); Vulnerability (computing); Scale (ratio); Population; Social equality; Environmental resource management; Context (archaeology); Risk analysis (engineering); Public economics; Environmental planning; Data science; Business; Computer science; Geography; Economics; Political science; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001212954,0.000457034,0.0004237634,0.002975958,0.001272857,0.0009694241,0.001751878,0.000661444,0.007024842],"category_scores_gemma":[0.006781759,0.0002245891,0.0007346258,0.005677432,0.0004015716,0.0004460425,0.001788067,0.0009772805,0.001958867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007290237,"about_ca_system_score_gemma":0.01414474,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8055865,"about_ca_topic_score_gemma":0.8800738,"domain_scores_codex":[0.9992681,0.0001295689,0.00005878888,0.000135244,0.0002639702,0.0001442132],"domain_scores_gemma":[0.9977731,0.000353672,0.0001718098,0.0004768855,0.0009906295,0.0002338613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003847328,0.0003312916,0.1584918,0.0007422433,0.0003412714,0.0005155534,0.001239535,0.03159212,0.001467595,0.02375411,0.6625715,0.1185683],"study_design_scores_gemma":[0.0002550842,0.00008118371,0.2806865,0.0006151281,0.0001350868,0.0003593054,0.003516854,0.0706729,0.00207567,0.01465936,0.6267466,0.0001963111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05121044,0.0002476079,0.01827592,0.0008804344,0.0001148357,0.0009682922,0.9146104,0.0008668473,0.01282513],"genre_scores_gemma":[0.09909073,0.0002662717,0.03121773,0.0002064143,0.00002866078,0.001330433,0.8644661,0.0001092208,0.003284392],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8055865,"threshold_uncertainty_score":0.3911169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0990967556283731,"score_gpt":0.4382109512222689,"score_spread":0.3391141955938958,"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."}}