{"id":"W2943428287","doi":"10.1017/cem.2019.305","title":"P114: Geographies of sexual assault: using geographic information system analysis to identify neighbourhoods affected by violence","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Public Health Policies and Education","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Census; Demography; Geography; Medicine; Poison control; Downtown; Sexual abuse; Injury prevention; Cartography; Population; Medical emergency; Environmental health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005863371,0.0002002625,0.0002236935,0.002490379,0.0006174931,0.00121624,0.0006109372,0.0003890089,0.006949957],"category_scores_gemma":[0.008047796,0.0001931469,0.000609111,0.005563205,0.0002319127,0.0008235042,0.001156928,0.0003757919,0.0007324266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006182118,"about_ca_system_score_gemma":0.001744055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1168012,"about_ca_topic_score_gemma":0.1931253,"domain_scores_codex":[0.9995821,0.0001220745,0.00006288528,0.00005045501,0.0001354511,0.00004701417],"domain_scores_gemma":[0.9972005,0.001131251,0.0007309472,0.0001889632,0.0005848953,0.0001634262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008147111,0.0000733802,0.9360257,0.0002457311,0.000197852,0.0002348222,0.001297649,0.001106458,0.0003468277,0.0007117382,0.02074211,0.03893622],"study_design_scores_gemma":[0.00001830626,0.00004163906,0.9858425,0.00009268041,0.0000844868,0.0001703903,0.004190889,0.002923396,0.0001958651,0.0006201525,0.005799151,0.00002059261],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9153087,0.0005347173,0.004246803,0.004867337,0.0002327453,0.0002817384,0.06087788,0.0002470268,0.01340302],"genre_scores_gemma":[0.9782679,0.0004232358,0.008260923,0.0001310908,0.00006166775,0.0001505216,0.01110802,0.00004025526,0.001556425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1168012,"threshold_uncertainty_score":0.2322426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06707274980556947,"score_gpt":0.4456596779694252,"score_spread":0.3785869281638558,"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."}}