{"id":"W3135174692","doi":"10.1371/journal.pone.0248285","title":"Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research","keywords":"Injury prevention; Metropolitan area; Poison control; Human factors and ergonomics; Occupational safety and health; Suicide prevention; Geography; Scale (ratio); Public health; Environmental health; Medicine; Cartography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002588242,0.0005299812,0.0004663799,0.004357621,0.001094898,0.00371948,0.001252123,0.000548725,0.001634961],"category_scores_gemma":[0.01302954,0.0003218649,0.0008838186,0.008117652,0.00203343,0.001321494,0.00245449,0.0007299136,0.0001622203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01134658,"about_ca_system_score_gemma":0.006538255,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.837252,"about_ca_topic_score_gemma":0.749012,"domain_scores_codex":[0.9982629,0.0009393775,0.000116897,0.0002891689,0.0002308076,0.0001608422],"domain_scores_gemma":[0.992734,0.00454329,0.001151836,0.0004810019,0.0007999825,0.0002899281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001098744,0.00007002855,0.4532747,0.0004086991,0.0004868564,0.00112108,0.004370864,0.3095179,0.0003653743,0.1691899,0.007141246,0.05394351],"study_design_scores_gemma":[0.00001410435,0.00004640714,0.1609714,0.0003384285,0.0001721555,0.0001631504,0.006663959,0.7645185,0.0002990182,0.04757912,0.01914819,0.0000855168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7391416,0.007830893,0.1965564,0.01226042,0.0001493184,0.0002839543,0.02189461,0.0004615116,0.02142128],"genre_scores_gemma":[0.9714144,0.00136344,0.02201873,0.00009793758,0.00004261916,0.00007430765,0.003156606,0.00003313955,0.001798708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.162748,"threshold_uncertainty_score":0.3274128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09278509176062444,"score_gpt":0.3610300194886277,"score_spread":0.2682449277280032,"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."}}