{"id":"W4205829323","doi":"10.2196/preprints.21958","title":"Impacts of COVID-19 on the health of illicit substance users : Preliminary Analysis from Illicit Drug Transaction Data. (Preprint)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Illicit drug; Coronavirus disease 2019 (COVID-19); Pandemic; Business; Sanctions; Database transaction; Drug; Internet privacy; Medicine; Pharmacology; Political science; Computer science; Law; Disease","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003911283,0.0004693034,0.0004195899,0.002470662,0.0003899314,0.001186941,0.0007462035,0.0008354895,0.008931823],"category_scores_gemma":[0.01784708,0.0002769735,0.001420567,0.004756295,0.0004780502,0.001377797,0.001811812,0.001051917,0.002144843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009769008,"about_ca_system_score_gemma":0.001508846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05318529,"about_ca_topic_score_gemma":0.04399347,"domain_scores_codex":[0.9967968,0.001592235,0.0003611975,0.0003102042,0.0005702107,0.000369338],"domain_scores_gemma":[0.9636737,0.02085372,0.009796435,0.001294078,0.003009462,0.001372613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003316472,0.0001259274,0.9829538,0.0003092973,0.0002960406,0.0002020829,0.0005895256,0.0007419062,0.0001117815,0.000154237,0.007780254,0.006403552],"study_design_scores_gemma":[0.00001464963,0.0002041117,0.9938453,0.00008231529,0.00009844045,0.00008062999,0.001635589,0.001669231,0.0001087942,0.00007918865,0.002168308,0.00001346675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8838001,0.0004828226,0.0009149764,0.001097399,0.00004825526,0.0003513281,0.1112531,0.00006861072,0.001983327],"genre_scores_gemma":[0.8933933,0.0005410537,0.001620951,0.0003685797,0.0001042033,0.000625859,0.1013297,0.00004177453,0.001974581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05318529,"threshold_uncertainty_score":0.1057514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08539112054104166,"score_gpt":0.3543848821448679,"score_spread":0.2689937616038262,"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."}}