{"id":"W4399828359","doi":"10.32920/26060821.v1","title":"The Relationship of Socio-Demographic and Health (Asthma, Diabetes, &amp; High Blood Pressure) in Toronto - a Quantitative and Spatial Approach","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo; Statistics Canada","funders":"","keywords":"Bivariate analysis; Socioeconomic status; Spatial analysis; Context (archaeology); Neighbourhood (mathematics); Multivariate statistics; Asthma; Demography; Medicine; Geography; Statistics; Environmental health; Mathematics; Sociology; Population","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":[0.0008352196,0.0001634906,0.0001718704,0.001969123,0.001108808,0.00132934,0.0004915438,0.0002173953,0.00196702],"category_scores_gemma":[0.005368401,0.0002290911,0.0003518692,0.005181342,0.0007796093,0.0004419237,0.001287535,0.0002676014,0.0001902963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01081015,"about_ca_system_score_gemma":0.005995676,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9038739,"about_ca_topic_score_gemma":0.9272932,"domain_scores_codex":[0.9990729,0.0002500045,0.0001037958,0.0001182437,0.0003174729,0.0001374826],"domain_scores_gemma":[0.9963974,0.001089954,0.001089973,0.0001928022,0.0007935646,0.000436358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000368272,0.00001204062,0.9821638,0.0001982905,0.00008332581,0.0001419786,0.008782313,0.0009407609,0.0003205279,0.001052349,0.001037104,0.005230693],"study_design_scores_gemma":[7.736957e-7,0.0000128582,0.9932984,0.00003428539,0.00002687928,0.00003032013,0.004762394,0.0003705465,0.00007018996,0.00005892223,0.001327859,0.000006725656],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985526,0.0007889272,0.0007508483,0.0004577846,0.0000124584,0.00003183685,0.007324522,0.00002155329,0.005086088],"genre_scores_gemma":[0.9974824,0.0003384391,0.0005286762,0.00001704877,0.000005825932,0.00003171659,0.0008821086,0.000003182222,0.0007107049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09612608,"threshold_uncertainty_score":0.1933843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06046752890077044,"score_gpt":0.3686100406189961,"score_spread":0.3081425117182257,"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."}}