{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00196852,0.001373128,0.0008051103,0.001007612,0.0002300481,0.0007848009,0.0008472581,0.0009266884,0.001534702],"category_scores_gemma":[0.005917767,0.0003087976,0.000561922,0.0005730737,0.0002621743,0.0005947123,0.0005930849,0.00105813,0.001153839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006518423,"about_ca_system_score_gemma":0.0007854294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001087338,"about_ca_topic_score_gemma":0.001907235,"domain_scores_codex":[0.999123,0.0003253754,0.00007378305,0.0002318929,0.0001760177,0.00006992051],"domain_scores_gemma":[0.9970145,0.001740336,0.000352534,0.0002616128,0.000538745,0.00009221279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009023004,0.001735674,0.03690295,0.0006685822,0.0003273341,0.0003821427,0.000109999,0.1801932,0.1804983,0.001525083,0.006234559,0.5905198],"study_design_scores_gemma":[0.00002023374,0.0002859543,0.004575687,0.00002074544,0.00003766191,0.0001357169,0.00001759808,0.9417344,0.05038682,0.001046803,0.00171622,0.00002203832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1894469,0.001576293,0.794944,0.0006882498,0.0001639788,0.0004103359,0.001127139,0.009440198,0.002202855],"genre_scores_gemma":[0.6562217,0.0005738306,0.3391298,0.0004641773,0.00007527593,0.0004458376,0.001264828,0.0001102682,0.001714291],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00196852,"threshold_uncertainty_score":0.01041067,"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."}}