{"id":"W2344296422","doi":"10.1007/978-3-319-41492-8_7","title":"Personalized Crime Location Prediction","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Apprehension; Law enforcement; Deterrence (psychology); Criminology; Enforcement; Demand reduction; Business; Crime prevention; Population; Computer security; Political science; Law; Computer science; Sociology; Psychology; Demography","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":[0.0003632753,0.00116048,0.001089359,0.003197271,0.0004847001,0.0009679976,0.001133934,0.0007949072,0.01182773],"category_scores_gemma":[0.001663125,0.0004005403,0.0008774695,0.002689031,0.0001410463,0.001097148,0.0008408385,0.0010037,0.009635222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005604335,"about_ca_system_score_gemma":0.0008074978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01629093,"about_ca_topic_score_gemma":0.03376674,"domain_scores_codex":[0.9996029,0.0000633142,0.000017372,0.0001615897,0.0000886613,0.00006618422],"domain_scores_gemma":[0.9995077,0.0001025255,0.00004752718,0.0001527275,0.0001418302,0.00004778154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004309816,0.0006186493,0.03522933,0.000165241,0.0002492552,0.0002072057,0.00005638377,0.05748123,0.0009702054,0.002988727,0.3265088,0.575094],"study_design_scores_gemma":[0.00005547562,0.0001241191,0.02449234,0.0001139852,0.0002143017,0.0004963708,0.0002541055,0.9124248,0.00334893,0.01447375,0.0439397,0.00006205945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2571233,0.006730262,0.406413,0.008392084,0.00296316,0.001006416,0.1927528,0.03824753,0.08637141],"genre_scores_gemma":[0.7420195,0.001951217,0.1144349,0.0005621538,0.0008715466,0.0003440162,0.1013462,0.0004240676,0.03804648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01629093,"threshold_uncertainty_score":0.03956771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02003357776702436,"score_gpt":0.2836312205048674,"score_spread":0.263597642737843,"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."}}