{"id":"W4281553254","doi":"10.3138/cjccj.2022-0004","title":"The Interurban Network of Criminal Collaboration in Canada","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interurban; Cluster (spacecraft); Geography; Population; Network structure; Criminology; Regional science; Sociology; Demography; Computer science; Transport engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005859226,0.0002928302,0.0003642824,0.003739523,0.005856048,0.002562649,0.001098797,0.0003048881,0.003984478],"category_scores_gemma":[0.00363627,0.0002720773,0.0003436094,0.009628944,0.001650782,0.0007849755,0.003313681,0.0005753305,0.0001999372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05391383,"about_ca_system_score_gemma":0.05660105,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951028,"about_ca_topic_score_gemma":0.9976017,"domain_scores_codex":[0.9987819,0.0001864774,0.00004676662,0.0001750329,0.0002440503,0.0005657739],"domain_scores_gemma":[0.9969943,0.0002667051,0.000601667,0.0001183102,0.001212849,0.0008062713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001518386,0.00006965848,0.8522992,0.0002005159,0.0001146522,0.0006423488,0.02011053,0.003599491,0.0003698065,0.02948897,0.02048967,0.07246334],"study_design_scores_gemma":[0.000009225025,0.00002173569,0.9467676,0.0001268541,0.00004449011,0.000238605,0.02705925,0.003402283,0.0001389112,0.002158422,0.01998543,0.00004720518],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9645848,0.001407821,0.001185534,0.00187333,0.0000360886,0.0001274717,0.004790934,0.00004809591,0.02594584],"genre_scores_gemma":[0.9952214,0.0005949097,0.0005306285,0.00004997557,0.00000373556,0.00003285655,0.001057209,0.000008414579,0.002500836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05391383,"threshold_uncertainty_score":0.3911741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07962246940862062,"score_gpt":0.3295968337341895,"score_spread":0.2499743643255689,"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."}}