{"id":"W1491984529","doi":"10.1111/gean.12047","title":"Analyzing Hotspots of Crime Using a<scp>B</scp>ayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in the<scp>G</scp>reater<scp>T</scp>oronto<scp>A</scp>rea","year":2014,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College; University of Waterloo","funders":"","keywords":"Frequentist inference; Law enforcement; Geography; Computer science; Population; Computer security; Bayesian probability; Demography; Bayesian inference; Artificial intelligence; Political science","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":[0.001742576,0.0003606146,0.0004286269,0.00230507,0.001030132,0.001283026,0.0008207514,0.000702237,0.0008768552],"category_scores_gemma":[0.002830424,0.0002678605,0.001474762,0.002792607,0.000675704,0.0009562944,0.001170055,0.0006680934,0.0001208425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001885527,"about_ca_system_score_gemma":0.0019059,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09576259,"about_ca_topic_score_gemma":0.1348497,"domain_scores_codex":[0.999305,0.0004263328,0.00003298854,0.00008291705,0.00008438681,0.00006843792],"domain_scores_gemma":[0.9987444,0.000726747,0.0002208933,0.0001129516,0.0001336707,0.0000613044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002673316,0.0007782683,0.4914888,0.0004538379,0.0009886493,0.009141578,0.01663774,0.3215859,0.002947958,0.05518605,0.005236505,0.09528743],"study_design_scores_gemma":[0.00003287674,0.0002284864,0.1408166,0.0001132763,0.000245889,0.0012137,0.02365778,0.8149204,0.001546032,0.01168179,0.005436313,0.0001068207],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9441951,0.0003662489,0.04543608,0.001131076,0.00001748081,0.00019655,0.0005947769,0.00006797435,0.007994741],"genre_scores_gemma":[0.9619324,0.0004405267,0.03579482,0.00004771556,0.000009554495,0.00009644812,0.0002438669,0.00001439949,0.001420391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9042374,"threshold_uncertainty_score":0.1904104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05704264313393517,"score_gpt":0.3319241703628593,"score_spread":0.2748815272289241,"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."}}