{"id":"W2149063544","doi":"","title":"Using Local Police Data to Inform Investigative Decision Making: A Study of Commercial Robbers' Spatial Decisions","year":2006,"lang":"en","type":"article","venue":"","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Suspect; Criminology; Geography; Crime analysis; Computer security; Sociology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005419551,0.0001010853,0.0001855437,0.0001838007,0.000550232,0.00008705418,0.0006733022,0.00005677556,0.00089493],"category_scores_gemma":[0.000292875,0.00009149012,0.00005609673,0.0005910991,0.0002169995,0.0003656483,0.0006163558,0.00009187705,0.00003230583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026362,"about_ca_system_score_gemma":0.0001282662,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4455778,"about_ca_topic_score_gemma":0.734916,"domain_scores_codex":[0.9984177,0.0001073285,0.0004661792,0.0002362008,0.0005356053,0.0002369725],"domain_scores_gemma":[0.9988675,0.0002805241,0.0001145246,0.0004511704,0.0001762369,0.0001100657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003412081,0.005991902,0.2218085,0.00001512318,0.0001698152,0.00001105627,0.1748329,0.005852534,0.0005445525,0.02529963,0.1041081,0.4610247],"study_design_scores_gemma":[0.002635908,0.001354704,0.6700894,0.0006565973,0.0002278446,0.000003493544,0.2644306,0.01325366,0.0003923681,0.005394574,0.04064891,0.0009119842],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.829185,0.00000324702,0.1588327,0.0002166525,0.0001851391,0.0003998637,0.00005095857,0.00002431686,0.01110212],"genre_scores_gemma":[0.9931664,4.253417e-7,0.006330045,0.0001588525,0.0001681848,0.000004048652,0.0000120545,0.000007608395,0.000152351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4601127,"threshold_uncertainty_score":0.9798856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3427898598899889,"score_gpt":0.4891137094989577,"score_spread":0.1463238496089688,"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."}}