{"id":"W1973175125","doi":"10.1016/j.aca.2014.10.007","title":"Ultra high performance supercritical fluid chromatography coupled with tandem mass spectrometry for screening of doping agents. II: Analysis of biological samples","year":2014,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":97,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"World Anti-Doping Agency; Univerzita Karlova v Praze","keywords":"Chemistry; Chromatography; Matrix (chemical analysis); Supercritical fluid chromatography; Mass spectrometry; Tandem mass spectrometry; Selectivity; Ion suppression in liquid chromatography–mass spectrometry; Analytical Chemistry (journal); High-performance liquid chromatography; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003285182,0.0004453537,0.001398437,0.0004716138,0.0002297719,0.00003395987,0.0006171663,0.0003152109,0.001201998],"category_scores_gemma":[0.0002714655,0.0003551764,0.0008715818,0.002048078,0.0007805781,0.0001276159,0.00007383594,0.0002835363,9.804384e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002922187,"about_ca_system_score_gemma":0.00004074632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001890159,"about_ca_topic_score_gemma":0.000004012531,"domain_scores_codex":[0.9971229,0.00003169214,0.0009201326,0.000710362,0.0005339512,0.0006809233],"domain_scores_gemma":[0.9977749,0.0007848293,0.000188057,0.0007363382,0.0002422512,0.0002736345],"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":[0.0002440649,0.0003338294,0.1339791,0.0004588875,0.004237761,0.00000151925,0.00004298574,0.00003789865,0.8584213,0.002146777,0.00006805107,0.00002786603],"study_design_scores_gemma":[0.001868886,0.0004498001,0.05551586,0.0003308255,0.006000485,0.00001169284,0.0003439666,0.08966229,0.8444231,0.0003619136,0.0001832842,0.0008479606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914643,0.00001610668,0.005567071,0.0001519735,0.00001092418,0.00009759059,0.000151607,0.00008528972,0.002455132],"genre_scores_gemma":[0.9919749,0.0001124889,0.007259657,0.0000664814,0.00009634998,0.0000179894,0.0004158036,0.00003622601,0.00002012475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08962439,"threshold_uncertainty_score":0.99989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02079550016564565,"score_gpt":0.2431355091410854,"score_spread":0.2223400089754398,"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."}}