{"id":"W4360592068","doi":"10.1007/978-3-031-27693-4_9","title":"Reducing Crime at High-Crime Places","year":2023,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in criminology","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Aside; Criminology; Core (optical fiber); Political science; Crime prevention; Control (management); Computer security; Public relations; Engineering; Computer science; Sociology; Artificial intelligence","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":[0.0002102254,0.0004902065,0.0002333444,0.0008019441,0.00051139,0.001338111,0.0008018891,0.0007791771,0.0326627],"category_scores_gemma":[0.0007716119,0.0001230334,0.0002041689,0.00104984,0.0005064146,0.0007453071,0.0007623037,0.001212461,0.005885076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006428745,"about_ca_system_score_gemma":0.001519291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01206262,"about_ca_topic_score_gemma":0.03624881,"domain_scores_codex":[0.9997744,0.00006214385,0.000004772919,0.00001970738,0.0000967178,0.00004222339],"domain_scores_gemma":[0.9998259,0.00005279153,0.00002549644,0.00001297255,0.0000397542,0.00004302736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001558056,0.0002598516,0.001363156,0.0002663065,0.00001519507,0.00002825073,0.0005013047,0.0006694388,0.0003852694,0.02649847,0.1925025,0.7774946],"study_design_scores_gemma":[0.00003476608,0.0001973892,0.04742158,0.001470478,0.00006203076,0.0002198707,0.003189119,0.001740234,0.001307621,0.09290352,0.8514286,0.00002480025],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.03248354,0.04372345,0.01068222,0.04324216,0.003243808,0.0001206106,0.0008389417,0.0003744448,0.8652908],"genre_scores_gemma":[0.2433759,0.09526207,0.01287811,0.006046404,0.002147185,0.0002510794,0.0007843354,0.0002824461,0.6389725],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.0326627,"threshold_uncertainty_score":0.1092676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1615594189265749,"score_gpt":0.3573993314798657,"score_spread":0.1958399125532908,"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."}}