{"id":"W7097154106","doi":"","title":"SEDAP A PROGRAM FOR RESEARCH ON SOCIAL AND ECONOMIC DIMENSIONS OF AN AGING POPULATION Geographic Dimensions of Aging in Canada","year":2003,"lang":"en","type":"article","venue":"","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Population ageing; Population; Field (mathematics); Dimension (graph theory)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005261089,0.000743293,0.00107853,0.003850938,0.01062258,0.003153294,0.003284152,0.001871209,0.01540101],"category_scores_gemma":[0.012887,0.0006118396,0.001482809,0.003175952,0.001320229,0.001226841,0.003327417,0.002184057,0.001179143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0871293,"about_ca_system_score_gemma":0.5169552,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9952706,"about_ca_topic_score_gemma":0.9979912,"domain_scores_codex":[0.9961594,0.0005806832,0.0001259643,0.0002655533,0.001739422,0.001128894],"domain_scores_gemma":[0.9692795,0.0023321,0.0005933556,0.0007933137,0.01810095,0.008900789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001097982,0.001184894,0.1545462,0.0006295885,0.0002411047,0.0003107533,0.001395815,0.001632399,0.001095006,0.00889164,0.6834279,0.1455467],"study_design_scores_gemma":[0.001558372,0.0008138305,0.6685287,0.001299063,0.0004817374,0.0001957932,0.00822844,0.006800953,0.002243478,0.004749703,0.3048879,0.0002120149],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2988024,0.007806968,0.007702645,0.1713274,0.00658309,0.01329414,0.3244163,0.001910413,0.1681567],"genre_scores_gemma":[0.5316485,0.009939578,0.03938329,0.03556399,0.00106253,0.01071215,0.05316344,0.0003737356,0.3181528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0871293,"threshold_uncertainty_score":0.6321703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1137255299713675,"score_gpt":0.4440130953832754,"score_spread":0.330287565411908,"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."}}