{"id":"W4391751773","doi":"10.1093/jat/bkae004","title":"Evaluation of the Canadian approved drug screening equipment cut-off levels for tetrahydrocannabinol (THC)","year":2024,"lang":"en","type":"article","venue":"Journal of Analytical Toxicology","topic":"Forensic Toxicology and Drug Analysis","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Canadian Mounted Police","funders":"","keywords":"Tetrahydrocannabinol; Driving under the influence; Drug detection; Chromatography; Medicine; Immunoassay; Whole blood; Drugs of abuse; Drug; Chemistry; Pharmacology; Poison control; Cannabinoid; Emergency medicine; Internal medicine; Immunology; Antibody; Injury prevention","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.009344702,0.0002760483,0.0006870121,0.0006449228,0.0003932832,0.00004157222,0.0006083917,0.0006551531,0.001478882],"category_scores_gemma":[0.001807383,0.0001962296,0.0007793338,0.0007215901,0.0007274806,0.0001620312,0.0000837421,0.001415203,0.00001961862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007299866,"about_ca_system_score_gemma":0.002978917,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003509948,"about_ca_topic_score_gemma":0.02266113,"domain_scores_codex":[0.9957494,0.001329971,0.001165991,0.0003373701,0.0007363851,0.0006808991],"domain_scores_gemma":[0.9966327,0.001039195,0.0004455308,0.0002641713,0.001143134,0.0004752754],"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":[0.001639494,0.001704687,0.007951575,0.0004832729,0.01808373,0.0002670209,0.003806232,0.09026176,0.02211369,0.07579137,0.2950914,0.4828058],"study_design_scores_gemma":[0.00355803,0.0007760937,0.004674824,0.000133135,0.01076935,0.0003710807,0.0004747391,0.6068866,0.02354813,0.01526341,0.3330781,0.0004665581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8918463,0.006985689,0.003768884,0.06648064,0.01028328,0.002042452,0.0002737689,0.00006422808,0.01825478],"genre_scores_gemma":[0.9952663,0.00004673325,0.0004239932,0.00190991,0.0005599903,0.00003003673,0.00000536465,0.00002741464,0.001730322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5166249,"threshold_uncertainty_score":0.9994339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1827322387192582,"score_gpt":0.4541639230324289,"score_spread":0.2714316843131707,"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."}}