{"id":"W2051187359","doi":"10.1016/j.aca.2013.11.056","title":"Introduction of solid-phase microextraction as a high-throughput sample preparation tool in laboratory analysis of prohibited substances","year":2013,"lang":"en","type":"article","venue":"Analytica Chimica Acta","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":101,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"World Anti-Doping Agency","keywords":"Chemistry; Chromatography; Solid-phase microextraction; Orbitrap; Sample preparation; Extraction (chemistry); Mass spectrometry; Triple quadrupole mass spectrometer; Solid phase extraction; Certified reference materials; Analytical Chemistry (journal); Detection limit; Gas chromatography–mass spectrometry; Selected reaction monitoring; Tandem mass spectrometry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004024337,0.0002658544,0.0007741111,0.0003824735,0.00004131705,0.00002808161,0.0002463682,0.0002161353,0.004811301],"category_scores_gemma":[0.001230758,0.0002651202,0.0001983407,0.002093528,0.0001717203,0.0003940005,0.0000547796,0.0002581137,0.00001809076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002580794,"about_ca_system_score_gemma":0.0001801375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003513292,"about_ca_topic_score_gemma":0.0001234215,"domain_scores_codex":[0.9973703,0.00007855265,0.001179556,0.0006104478,0.0004382379,0.000322886],"domain_scores_gemma":[0.9978594,0.0003439121,0.0005629583,0.000672001,0.0004790009,0.00008268714],"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":[0.0002120281,0.0006540095,0.002036991,0.0001645856,0.001012848,0.000001050338,0.0002766557,0.00005099123,0.9944683,0.0002441316,0.000625697,0.0002527228],"study_design_scores_gemma":[0.0007194756,0.00005528303,0.004405819,0.00003838453,0.001019422,0.000001521071,0.0002661188,0.01472713,0.9771065,0.0007050991,0.0006630162,0.0002922516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973981,0.00001319715,0.001219784,0.000595432,0.00002925619,0.0001778778,0.0001233291,0.00004218725,0.0004008692],"genre_scores_gemma":[0.988424,0.00006057457,0.0105196,0.0000316477,0.0001191856,0.00004280088,0.0004846267,0.00002164352,0.0002958876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01736181,"threshold_uncertainty_score":0.9999801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346669983190823,"score_gpt":0.3262466469639862,"score_spread":0.312779947132078,"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."}}