{"id":"W2465721551","doi":"10.1111/gean.12107","title":"Persistence of Crime Hot Spots: An Ordered Probit Analysis","year":2016,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Persistence (discontinuity); Probit model; Probit; Criminology; Hot spot (computer programming); Spots; Econometrics; Psychology; Economics; Computer science; Engineering; Biology","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":[0.005609258,0.00061082,0.001097964,0.003554762,0.0009324423,0.002407543,0.001587667,0.001052327,0.004412463],"category_scores_gemma":[0.02000346,0.000445976,0.001415115,0.003109616,0.001193163,0.001633697,0.001800168,0.002062176,0.0006085861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001452158,"about_ca_system_score_gemma":0.001328885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04748799,"about_ca_topic_score_gemma":0.01886097,"domain_scores_codex":[0.9964272,0.001986984,0.0001964587,0.0005602666,0.000389077,0.0004398908],"domain_scores_gemma":[0.9799353,0.01522715,0.00260671,0.0008875772,0.0008085052,0.0005348308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004696687,0.000464719,0.8778189,0.0001538853,0.001018695,0.00106658,0.002093296,0.07041214,0.0004085806,0.01381158,0.002700885,0.02958105],"study_design_scores_gemma":[0.00004267122,0.0004100444,0.2237788,0.00007995851,0.0005052008,0.0003823865,0.003180164,0.7535573,0.0003072905,0.01540104,0.002265755,0.00008933109],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9646505,0.0003223336,0.02958619,0.0008744381,0.00004255183,0.000125101,0.00190446,0.0001713843,0.002323098],"genre_scores_gemma":[0.9960117,0.0001076785,0.002119769,0.00002735281,0.00002111949,0.00006004258,0.0007276279,0.00001672739,0.0009079363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04748799,"threshold_uncertainty_score":0.09442317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04567176258293684,"score_gpt":0.3346889523344053,"score_spread":0.2890171897514685,"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."}}