{"id":"W3109924985","doi":"10.5683/sp2/ie6nry","title":"Calgary Crime Statistics Data Analytics","year":2019,"lang":"en","type":"dataset","venue":"Borealis","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Law enforcement; Harm; Criminology; Crime statistics; Analytics; Population; Order (exchange); Enforcement; Internet privacy; Psychology; Data science; Political science; Sociology; Business; Law; Computer science; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001287361,0.002171173,0.001029501,0.005537352,0.001293485,0.002238984,0.002961759,0.001470192,0.01855612],"category_scores_gemma":[0.007949583,0.0006947738,0.0007329636,0.009102069,0.0005030824,0.001068731,0.00198114,0.002335325,0.03291334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003796306,"about_ca_system_score_gemma":0.005232603,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3330783,"about_ca_topic_score_gemma":0.3547915,"domain_scores_codex":[0.9985579,0.0001782184,0.0001585122,0.0003103015,0.0005484216,0.0002466528],"domain_scores_gemma":[0.9944369,0.0006736689,0.0004071115,0.001301206,0.002625733,0.0005554399],"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":[0.00002474552,0.00001978032,0.00124127,0.0001006356,0.00001149279,0.0000269361,0.00005725738,0.0002383143,0.0000688866,0.0004551635,0.9942298,0.00352577],"study_design_scores_gemma":[0.0001430933,0.00002141873,0.01928029,0.0002041129,0.00001843134,0.00006808792,0.0004176228,0.003142365,0.0008326921,0.001247949,0.9745745,0.00004935277],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000993146,0.00006477561,0.0001951487,0.0002271004,0.00005146686,0.0000538232,0.9951115,0.00139289,0.001910078],"genre_scores_gemma":[0.001529431,0.00007327776,0.0009175917,0.0000511813,0.0000143741,0.0001245436,0.9958537,0.0001308443,0.001305131],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6669217,"threshold_uncertainty_score":0.6622791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.069596425181322,"score_gpt":0.3197901531367368,"score_spread":0.2501937279554148,"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."}}