{"id":"W2891747940","doi":"10.1177/0091450918797355","title":"Six Years Later","year":2018,"lang":"en","type":"article","venue":"Contemporary Drug Problems","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Cannabis; Business; Advertising; Enforcement; Herbal supplement; Scope (computer science); Drug prices; Law enforcement; Commerce; Medicine; Economics; Monetary economics; Psychiatry; Alternative medicine; Law; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002604654,0.0001331499,0.0001563542,0.00006489518,0.0001507409,0.0001140581,0.0006213166,0.00003182871,0.00006527548],"category_scores_gemma":[0.000008519051,0.0001165394,0.00006173472,0.0002082588,0.0001164934,0.0006153497,0.0004136187,0.00008375775,0.0008809534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000159817,"about_ca_system_score_gemma":0.00005134345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001843427,"about_ca_topic_score_gemma":0.00004789883,"domain_scores_codex":[0.9989392,0.00003628811,0.0002426423,0.0003350994,0.0001933427,0.0002534933],"domain_scores_gemma":[0.9992712,0.00002866231,0.00006178361,0.0004948211,0.00007192842,0.00007160028],"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":[0.00002090844,0.0001782932,0.01558311,0.00006738537,0.0001813275,0.00005937851,0.0187016,0.00001137,0.001454649,0.1224093,0.8092084,0.03212424],"study_design_scores_gemma":[0.0005552873,0.0001175118,0.005503906,0.00004610044,0.000003802347,0.000004448921,0.00005028531,0.0007340502,0.002766277,0.00349358,0.9864246,0.0003001373],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1421373,0.002692113,0.02536669,0.005982006,0.002749147,0.001068147,0.000006970294,0.001293427,0.8187042],"genre_scores_gemma":[0.9854935,0.00001249851,0.0009530296,0.001096024,0.0001775477,0.0000230049,0.000003491043,0.00001058577,0.0122303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8433563,"threshold_uncertainty_score":0.999897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02929019299200632,"score_gpt":0.2477677945093299,"score_spread":0.2184776015173236,"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."}}