{"id":"W7036965017","doi":"","title":"A Critical Assessment of Mr. Big Operations by Canada's Police","year":2020,"lang":"en","type":"article","venue":"Arca (British Columbia Electronic Library Network)","topic":"Subterranean biodiversity and taxonomy","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Law enforcement; Injustice; Big data; Enforcement; Criminal law; Criminal justice","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004331574,0.0007616193,0.0003577448,0.006022851,0.05221233,0.009923625,0.003119494,0.004039973,0.005347622],"category_scores_gemma":[0.01589075,0.0006291766,0.0005035916,0.005317743,0.007743177,0.002624694,0.003003215,0.007107646,0.0004737642],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1523127,"about_ca_system_score_gemma":0.3239281,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9938653,"about_ca_topic_score_gemma":0.9980701,"domain_scores_codex":[0.9903474,0.0007945329,0.0001709441,0.0003760359,0.004766961,0.003544133],"domain_scores_gemma":[0.9858909,0.001246377,0.0004696062,0.0002098563,0.01009166,0.002091677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00008703618,0.00008381018,0.02450182,0.0005542984,0.00004473616,0.005392868,0.1629644,0.0006012903,0.001157267,0.1264299,0.5688301,0.1093524],"study_design_scores_gemma":[0.000007708504,0.00004018378,0.03670759,0.0006467465,0.00004007295,0.0004447767,0.2909448,0.0003849615,0.0005707428,0.003931418,0.6661724,0.0001084866],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1976533,0.01200623,0.002155106,0.3252657,0.004189217,0.0007705453,0.001483895,0.0001706717,0.4563052],"genre_scores_gemma":[0.842208,0.01119915,0.002449785,0.04119096,0.0005045186,0.000144627,0.0004372647,0.00009737782,0.1017683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1523127,"threshold_uncertainty_score":0.9831971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01225271338305562,"score_gpt":0.1764131053098967,"score_spread":0.1641603919268411,"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."}}