{"id":"W4392968039","doi":"10.32920/25417597","title":"Open Geodemographics and Crime: A Case Study Of Toronto","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Criminology; Political science; Business; 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":[0.0008483317,0.0004862227,0.0004403604,0.001752285,0.01862689,0.002725218,0.001470309,0.001429136,0.005787329],"category_scores_gemma":[0.003541995,0.0003972976,0.0004151842,0.005799081,0.005265368,0.00118791,0.003661643,0.001829177,0.0003232765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04089965,"about_ca_system_score_gemma":0.0195659,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9153919,"about_ca_topic_score_gemma":0.9718356,"domain_scores_codex":[0.9978789,0.0009180996,0.00007611963,0.0001389938,0.0002919968,0.0006958538],"domain_scores_gemma":[0.9974493,0.0008251655,0.0003906419,0.0001882048,0.0003484306,0.0007982581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002170084,0.0004964307,0.1465557,0.0004259567,0.000105188,0.03162054,0.7477115,0.00180233,0.0008074752,0.02466235,0.01578362,0.02981182],"study_design_scores_gemma":[0.00001629593,0.0001116437,0.1074127,0.0002712875,0.00005416326,0.002035712,0.8623512,0.001044574,0.0003333255,0.000968132,0.02535039,0.00005058972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813036,0.0004196975,0.0004197579,0.00241873,0.00002705679,0.0001137483,0.0004253453,0.00001197228,0.01486017],"genre_scores_gemma":[0.9933603,0.0006949473,0.0004091716,0.0002046866,0.00001261915,0.00005396949,0.000181385,0.00001539949,0.0050674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08460814,"threshold_uncertainty_score":0.2967491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1417165305859413,"score_gpt":0.4801868170845466,"score_spread":0.3384702864986053,"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."}}