{"id":"W1950720201","doi":"10.1002/dta.1727","title":"Direct analysis in real time ‐ high resolution mass spectrometry (DART‐HRMS): a high throughput strategy for identification and quantification of anabolic steroid esters","year":2014,"lang":"en","type":"article","venue":"Drug Testing and Analysis","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"World Anti-Doping Agency","keywords":"DART ion source; Orbitrap; Chemistry; Chromatography; Mass spectrometry; Repeatability; Dart; Steroid; Hormone; Ion; Electron ionization; Computer science; Biochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0007805337,0.0001979444,0.0005932394,0.0009554612,0.0001527485,0.0001072838,0.0001582821,0.0001013077,0.00005934785],"category_scores_gemma":[0.0002173995,0.0002029394,0.0001547016,0.003633706,0.00008764545,0.0001222552,0.00003316901,0.0001043365,9.848337e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006518733,"about_ca_system_score_gemma":0.0000153477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002370851,"about_ca_topic_score_gemma":0.0002038606,"domain_scores_codex":[0.9982058,0.00006845686,0.0006407395,0.0006367431,0.000199344,0.0002488482],"domain_scores_gemma":[0.9982513,0.000488307,0.0005137066,0.0005384797,0.0001355942,0.000072571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003948314,0.0002136139,0.1813967,0.0002628712,0.001369175,6.807277e-7,0.0001590449,0.006793032,0.7963191,0.006141389,0.00004911275,0.007255846],"study_design_scores_gemma":[0.0006570579,0.00007378784,0.3238191,0.00006062412,0.006043516,0.000001518395,0.0002303852,0.5613587,0.0988263,0.008342577,0.00003464403,0.0005517493],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9566963,0.00007959636,0.04123409,0.0001579902,0.000005434621,0.0001381987,0.0001381511,0.0001387732,0.001411454],"genre_scores_gemma":[0.9802559,0.00009535461,0.01862707,0.000005045337,0.00004358122,0.00006779387,0.0005121692,0.00001753591,0.0003755351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6974927,"threshold_uncertainty_score":0.827563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01856610771981325,"score_gpt":0.274956349825463,"score_spread":0.2563902421056498,"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."}}