{"id":"W3203098898","doi":"10.1007/s13278-021-00796-2","title":"Copresence networks: criminals in their environment","year":2021,"lang":"en","type":"article","venue":"Social Network Analysis and Mining","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Law enforcement; Computer security; Demographics; Leverage (statistics); Criminology; Enforcement; Profiling (computer programming); Computer science; Social network analysis; Organised crime; Social network (sociolinguistics); Internet privacy; Sociology; Political science; Artificial intelligence; Law; World Wide Web; Social media; Demography","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":[0.0007278274,0.000121477,0.0003743284,0.00004885121,0.0007670336,0.0001158632,0.0001140698,0.0001117903,0.0002169416],"category_scores_gemma":[0.00006024909,0.0001269295,0.0001682607,0.0009657186,0.0002308456,0.0001292073,0.00007998573,0.0001381616,0.000001565282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008097029,"about_ca_system_score_gemma":0.00007052795,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002732762,"about_ca_topic_score_gemma":0.02537153,"domain_scores_codex":[0.9984258,0.0003008674,0.0002289149,0.0003188321,0.0002569594,0.0004686104],"domain_scores_gemma":[0.999342,0.0003061557,0.0001305599,0.0001119076,0.00002857817,0.00008085483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005812474,0.0002365996,0.5523073,0.00002047651,0.001289237,0.000129432,0.1800758,0.03717692,0.00005879545,0.02390246,0.01195725,0.1927876],"study_design_scores_gemma":[0.0007549238,0.00004562645,0.381323,0.0001152788,0.001344802,0.000001305071,0.203925,0.01373558,0.00004801504,0.004657603,0.3928906,0.001158276],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.957078,0.0103408,0.006000598,0.001456537,0.0004443452,0.0001352342,0.00001010424,0.00004019179,0.02449423],"genre_scores_gemma":[0.9921086,0.004426445,0.0002741541,0.0002808365,0.001091277,0.00001004447,0.00001003299,0.000007978936,0.001790641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3809334,"threshold_uncertainty_score":0.9924129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0262837285070825,"score_gpt":0.281731291238226,"score_spread":0.2554475627311436,"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."}}