{"id":"W4391446396","doi":"10.21810/jicw.v6i3.6397","title":"LESSONS LEARNED: HOW FENTANYL IS IMPACTING ORGANIZED CRIME IN NORTH AMERICA","year":2024,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fentanyl; Criminology; Political science; Psychology; Medicine; Anesthesia","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.003951712,0.0003312126,0.0003532328,0.000745204,0.003644563,0.005095361,0.0009812785,0.003321206,0.008861228],"category_scores_gemma":[0.01002365,0.0002167763,0.0006350027,0.0008179328,0.004254474,0.004013368,0.002542145,0.005839485,0.0008294378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006660692,"about_ca_system_score_gemma":0.01615281,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07647254,"about_ca_topic_score_gemma":0.1858188,"domain_scores_codex":[0.9959848,0.002069561,0.000149307,0.0002143841,0.0007285782,0.0008533374],"domain_scores_gemma":[0.9934438,0.002588259,0.0004327319,0.0001783692,0.001585905,0.001770881],"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":[0.0001826409,0.0004943454,0.01920452,0.001522105,0.0001282576,0.001400932,0.02769448,0.0008199009,0.0005449101,0.03413431,0.5396155,0.3742582],"study_design_scores_gemma":[0.00006618476,0.0004367265,0.0416947,0.004890902,0.00009069102,0.0007301904,0.106904,0.0004333079,0.0008738326,0.04103534,0.8027459,0.00009824722],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01896929,0.02245406,0.0004027091,0.9215665,0.00327429,0.00003284899,0.0001031534,0.00003850033,0.03315872],"genre_scores_gemma":[0.507654,0.1286924,0.002052299,0.3203902,0.005234852,0.0001549668,0.0002243487,0.00009129087,0.03550569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9235275,"threshold_uncertainty_score":0.1520548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0884959316611582,"score_gpt":0.3698566549929788,"score_spread":0.2813607233318206,"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."}}