{"id":"W7006777139","doi":"","title":"What is the impact of social media on law enforcement practices?","year":2017,"lang":"en","type":"other","venue":"Arca (British Columbia Electronic Library Network)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Credibility; Law enforcement; Social media; Scope (computer science); Enforcement; Data collection","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":[0.002818757,0.0002527582,0.0003443469,0.005563041,0.004021401,0.009693932,0.001077552,0.0009474827,0.006207578],"category_scores_gemma":[0.02525635,0.0002487663,0.0003798724,0.00663014,0.004131262,0.003960445,0.002001421,0.001024285,0.0004912217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01256175,"about_ca_system_score_gemma":0.0168092,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.575318,"about_ca_topic_score_gemma":0.6103014,"domain_scores_codex":[0.9943929,0.001615495,0.0002139833,0.0003072635,0.0025374,0.0009330455],"domain_scores_gemma":[0.9739615,0.01067838,0.006552095,0.0005345609,0.005748132,0.002525348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001350926,0.0004325274,0.6275024,0.001345552,0.0002455243,0.0008855369,0.1118848,0.000228247,0.0005786451,0.007594423,0.007978779,0.2411883],"study_design_scores_gemma":[0.00001082199,0.00008314248,0.7555108,0.001903064,0.00009598431,0.0002524195,0.2024003,0.0002644959,0.000390098,0.0009591108,0.03808871,0.00004111685],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8580363,0.0110185,0.0002800132,0.02252273,0.000182929,0.00009240479,0.0007585061,0.00003745213,0.1070712],"genre_scores_gemma":[0.9903374,0.00656086,0.0001324547,0.000549897,0.00009535867,0.00001734934,0.000105094,0.00001220298,0.00218942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.575318,"threshold_uncertainty_score":0.8543661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01373700723051121,"score_gpt":0.2673382299380309,"score_spread":0.2536012227075197,"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."}}