{"id":"W1975514992","doi":"10.1080/15389580903191617","title":"Toward a More Parsimonious Approach to Drug Recognition Expert Evaluations","year":2009,"lang":"en","type":"article","venue":"Traffic Injury Prevention","topic":"Forensic Toxicology and Drug Analysis","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Centre on Substance Use and Addiction","funders":"National Highway Traffic Safety Administration","keywords":"Drug; Medicine; Cannabis; Multinomial logistic regression; Stimulant; Logistic regression; Narcotic; Poison control; Machine learning; Medical emergency; Anesthesia; Pharmacology; Computer science; Psychiatry; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001188436,0.0002707409,0.0003276771,0.0002989011,0.0003732291,0.00002668275,0.0002396447,0.0003812153,0.0008232422],"category_scores_gemma":[0.000105737,0.0002760548,0.0002625512,0.0005668218,0.0001146566,0.0001969778,0.00002947768,0.0005726356,0.0006204616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001313667,"about_ca_system_score_gemma":0.00009605708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005515475,"about_ca_topic_score_gemma":0.000009628155,"domain_scores_codex":[0.9975542,0.0007116702,0.00048931,0.0005212841,0.000248888,0.0004746527],"domain_scores_gemma":[0.9990904,0.00009031575,0.0001494813,0.000264051,0.0001486078,0.0002570929],"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":[0.0009067449,0.002714438,0.00003977335,0.00002574821,0.0003044813,0.000009426966,0.008209766,0.01154703,0.004450201,0.0008303977,0.08743718,0.8835248],"study_design_scores_gemma":[0.02702938,0.009732395,0.02669478,0.0005883849,0.01050607,0.00048987,0.02089084,0.3591479,0.1534969,0.05561316,0.326391,0.009419261],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784999,0.0003704427,0.003548339,0.004996102,0.0008568369,0.001108361,0.00005567417,0.0004161585,0.01014822],"genre_scores_gemma":[0.9884223,0.0001013929,0.003323941,0.004772708,0.0003225463,0.0001915396,0.0003612691,0.00001895087,0.002485307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8741056,"threshold_uncertainty_score":0.9999692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1315815803643343,"score_gpt":0.4521482125332031,"score_spread":0.3205666321688688,"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."}}