{"id":"W4210446778","doi":"10.1093/clinchem/hvac027","title":"Machine Learning to Assist in Large-Scale, Activity-Based Synthetic Cannabinoid Receptor Agonist Screening of Serum Samples","year":2022,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Forensic Toxicology and Drug Analysis","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Machine learning; Cannabinoid receptor; Artificial intelligence; Cannabinoid; Drug discovery; Agonist; Computer science; Medicine; Receptor; Pharmacology; Bioinformatics; Biology; Internal medicine","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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003110822,0.0002615176,0.0007105666,0.0000997785,0.0005113767,0.000009999794,0.0004612235,0.0004348278,0.008351713],"category_scores_gemma":[0.001187878,0.0002888938,0.0003659034,0.000582978,0.0004006577,0.00003427635,0.000371976,0.002948625,0.00002812038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001238899,"about_ca_system_score_gemma":0.0002256719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004731589,"about_ca_topic_score_gemma":0.0001161186,"domain_scores_codex":[0.9965116,0.001200529,0.0008295727,0.0006347136,0.0002422962,0.0005812919],"domain_scores_gemma":[0.9974706,0.00141732,0.0003597825,0.0003448446,0.00006574841,0.0003417116],"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":[0.001905909,0.002034284,0.9291921,0.0001618072,0.0002495519,0.00006044472,0.0002449994,0.02895903,0.01829489,0.00002939492,0.002285575,0.016582],"study_design_scores_gemma":[0.008891373,0.0008849755,0.08817506,0.000090551,0.0007794492,0.00002846785,0.001825508,0.07841114,0.3051809,0.0001300857,0.5141816,0.001420985],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926409,0.0001935833,0.0002532809,0.004122281,0.0004032291,0.0001812588,0.0007095985,0.00007922064,0.001416724],"genre_scores_gemma":[0.9947186,0.00001650163,0.0006146207,0.001708254,0.0001403797,0.00008449434,0.000133146,0.00003169403,0.002552375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8410171,"threshold_uncertainty_score":0.9999563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08427117621343658,"score_gpt":0.4201394234695732,"score_spread":0.3358682472561366,"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."}}