{"id":"W4245506753","doi":"10.1017/s0956792516000140","title":"Personalized crime location prediction","year":2016,"lang":"en","type":"article","venue":"European Journal of Applied Mathematics","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Law enforcement; Criminology; Geography; Crime analysis; Space (punctuation); Enforcement; Economic geography; Computer science; Sociology; Political science; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00130654,0.0000579495,0.000105603,0.00006721039,0.0001321838,0.00004177156,0.0001832888,0.00001423045,0.001274373],"category_scores_gemma":[0.000121206,0.00003819032,0.00008701257,0.00008432747,0.0001132527,0.0001171444,0.00001753031,0.0000613061,0.0002209224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005708664,"about_ca_system_score_gemma":0.00003986173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001444405,"about_ca_topic_score_gemma":0.000001339896,"domain_scores_codex":[0.9990914,0.00007328772,0.0003839081,0.00005773659,0.0002830907,0.0001105486],"domain_scores_gemma":[0.9992369,0.0000671732,0.0003397524,0.00009031265,0.0001865875,0.00007927716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009932608,0.0008983894,0.0002253799,0.0001516897,0.000226833,0.00003683686,0.09158195,0.00001260938,0.04013679,0.6749035,0.08045668,0.11127],"study_design_scores_gemma":[0.00596426,0.0008371215,0.008134503,0.002589969,0.0004809337,0.0001001879,0.04811259,0.00007877107,0.009010201,0.05103737,0.8729206,0.0007334976],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08485074,0.00007029473,0.3738346,0.00118948,0.0004867349,0.0001720651,0.000007539259,0.00005455917,0.5393339],"genre_scores_gemma":[0.9894837,0.00007065821,0.007743296,0.0000464214,0.0004901337,8.525811e-7,3.940988e-7,0.00001632514,0.002148237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9046329,"threshold_uncertainty_score":0.9996386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05005557464595303,"score_gpt":0.3087733891191068,"score_spread":0.2587178144731538,"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."}}