{"id":"W2168511004","doi":"10.1109/isi.2015.7165934","title":"Learning where to inspect: Location learning for crime prediction","year":2015,"lang":"en","type":"article","venue":"","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Commit; Crime analysis; Crime scene; Computer science; Space (punctuation); Probabilistic logic; Criminology; Geography; Artificial intelligence; Sociology","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":[],"consensus_categories":[],"category_scores_codex":[0.000572582,0.00004437649,0.00005755205,0.00005676497,0.0003841689,0.00009118408,0.00007353793,0.00004146369,0.0005956964],"category_scores_gemma":[0.0005393518,0.00004499697,0.00004267207,0.0001645517,0.0000217544,0.0001948902,0.00001932293,0.0000820352,0.0001956413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001154042,"about_ca_system_score_gemma":0.0000603749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004124751,"about_ca_topic_score_gemma":0.002731326,"domain_scores_codex":[0.9993653,0.00007543166,0.0001212897,0.0001283231,0.0001571092,0.00015257],"domain_scores_gemma":[0.9994956,0.00003103196,0.00003576629,0.00004881908,0.0002688119,0.000120031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001074741,0.0003473105,0.2082698,0.00008290634,0.00007422866,0.00000109168,0.1554192,0.01842831,0.001333885,0.08980566,0.4072331,0.1188971],"study_design_scores_gemma":[0.000299468,0.0005405508,0.009800278,0.00005577293,0.00001585054,3.665353e-7,0.04398641,0.003173948,0.0002027644,0.0006010098,0.9412003,0.0001232785],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2334771,0.0001667387,0.3685998,0.00506226,0.001113849,0.0008229507,0.000003982409,0.000652301,0.390101],"genre_scores_gemma":[0.9425536,0.000006120174,0.0005185942,0.00005405966,0.0003301436,0.00003353273,0.00001051533,0.000007519024,0.05648594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7090765,"threshold_uncertainty_score":0.6522458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08771824339523497,"score_gpt":0.3870670428744646,"score_spread":0.2993487994792296,"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."}}