{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0005694797,0.0004240016,0.0006729906,0.0003431716,0.00008343918,0.0005810831,0.01248092,0.0002754527,0.0001087826],"category_scores_gemma":[0.0004221045,0.0003903623,0.00007722394,0.0005080002,0.00008074415,0.000695051,0.005106673,0.0004902431,0.0005946594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008142288,"about_ca_system_score_gemma":0.0005336917,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04118585,"about_ca_topic_score_gemma":0.01131476,"domain_scores_codex":[0.9965048,0.0001359752,0.0005831747,0.001297006,0.0009877209,0.0004912912],"domain_scores_gemma":[0.984951,0.0002662456,0.000430342,0.01395315,0.0001834654,0.0002158269],"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":[0.000001809083,0.00005251651,0.000004605673,0.00007389715,0.0001751954,0.0001649026,0.000004738096,0.000007563907,1.335557e-7,0.001125207,0.9965541,0.001835307],"study_design_scores_gemma":[0.0001223616,0.00003262217,0.0001627939,0.00003073618,0.0003572995,0.0000137347,0.000001558587,0.03705573,9.797147e-7,0.0002575265,0.9615182,0.0004464444],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[6.897763e-9,0.000118531,0.2233137,0.00008881553,0.000298444,0.0001307825,0.775687,0.00006093376,0.0003018188],"genre_scores_gemma":[8.545691e-8,0.0003618861,0.04350174,0.0009217131,0.0002040203,0.000004410532,0.954678,0.00002141847,0.0003067118],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.179812,"threshold_uncertainty_score":0.9998548,"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."}}