{"id":"W2098068920","doi":"10.1186/s13065-014-0073-0","title":"Hair-based rapid analyses for multiple drugs in forensics and doping: application of dynamic multiple reaction monitoring with LC-MS/MS","year":2014,"lang":"en","type":"article","venue":"Chemistry Central Journal","topic":"Forensic Toxicology and Drug Analysis","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"World Anti-Doping Agency","keywords":"Chromatography; Chemistry; Hair analysis; Analyte; Liquid chromatography–mass spectrometry; Extraction (chemistry); Bioanalysis; Forensic toxicology; Mass spectrometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002532868,0.001370484,0.0007725194,0.002340655,0.0005226775,0.0006988861,0.0009743334,0.001327842,0.002445387],"category_scores_gemma":[0.001707603,0.0004820969,0.0007136471,0.0009726417,0.0007935583,0.0006879711,0.0009654141,0.001011901,0.001524514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004677218,"about_ca_system_score_gemma":0.0008702016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004895677,"about_ca_topic_score_gemma":0.001135047,"domain_scores_codex":[0.9967324,0.0006144452,0.000124043,0.000917886,0.001452615,0.0001586715],"domain_scores_gemma":[0.9989907,0.0002854429,0.0002455857,0.00007525,0.0003329165,0.00007008461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002801083,0.0001637912,0.004835086,0.0009451745,0.0001886101,0.0002803651,0.00006470735,0.0004493224,0.921375,0.0004935719,0.000940885,0.06998339],"study_design_scores_gemma":[0.00008877187,0.002160467,0.01695907,0.0002786967,0.0002941985,0.004513291,0.0001260165,0.01272347,0.9383283,0.001104253,0.02327033,0.0001531144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3732607,0.07030969,0.5272611,0.00150942,0.001003233,0.002150683,0.004174931,0.004670263,0.01565988],"genre_scores_gemma":[0.5514689,0.02319353,0.4135306,0.002011149,0.0004724803,0.001058649,0.001362279,0.0002935097,0.006609026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002532868,"threshold_uncertainty_score":0.01339525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04643523339102899,"score_gpt":0.379393959225612,"score_spread":0.332958725834583,"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."}}