{"id":"W3034173132","doi":"10.1021/acs.analchem.0c00877","title":"Fast Quantification Without Conventional Chromatography, The Growing Power of Mass Spectrometry","year":2020,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Bildung und Forschung; Ministry of Agriculture - Saskatchewan","keywords":"Chemistry; Ion-mobility spectrometry; Chromatography; Mass spectrometry; Quantitative analysis (chemistry); Ion suppression in liquid chromatography–mass spectrometry; Analytical technique; Resolution (logic); Matrix (chemical analysis); Sample preparation; Tandem mass spectrometry; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008784662,0.002380147,0.002506408,0.00362798,0.001038085,0.004604457,0.002763196,0.003122115,0.002462324],"category_scores_gemma":[0.01053988,0.0009988658,0.001127766,0.003104545,0.003680703,0.006994519,0.003653674,0.005717421,0.003306834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001723672,"about_ca_system_score_gemma":0.003585127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001269408,"about_ca_topic_score_gemma":0.001610384,"domain_scores_codex":[0.9882658,0.002214395,0.0004767103,0.002964452,0.005738268,0.0003404809],"domain_scores_gemma":[0.9942508,0.001950222,0.0006623149,0.0008826032,0.002027381,0.000226708],"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":[0.0003102626,0.000147395,0.002695883,0.006824446,0.0004529271,0.0005229252,0.0004505168,0.002315041,0.5340775,0.05516448,0.02105705,0.3759816],"study_design_scores_gemma":[0.00004111999,0.0005423697,0.002504987,0.001377504,0.000338712,0.001896862,0.0004346855,0.01592677,0.5301709,0.05654822,0.3898452,0.0003726045],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01031459,0.1359152,0.8307188,0.005628487,0.003558526,0.0003585743,0.001591574,0.002975044,0.008939226],"genre_scores_gemma":[0.08175861,0.1456212,0.7511205,0.004679035,0.003275699,0.0007648488,0.002189761,0.0009077577,0.009682458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008784662,"threshold_uncertainty_score":0.0464583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01721673493060343,"score_gpt":0.2667997146935127,"score_spread":0.2495829797629093,"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."}}