{"id":"W2948523541","doi":"10.1021/acs.analchem.9b01625","title":"Quantitative Analysis of Protein Covalent Labeling Mass Spectrometry Data in the Mass Spec Studio","year":2019,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Chemistry; Mass spectrometry; Normalization (sociology); Tandem mass tag; Tandem mass spectrometry; Isobaric labeling; Data mining; Biological system; Proteomics; Chromatography; Quantitative proteomics; Computer science; Biochemistry; Protein 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006451678,0.00149016,0.001353594,0.002046936,0.0005777896,0.003018165,0.001837852,0.000814199,0.01118584],"category_scores_gemma":[0.006985573,0.0007104977,0.001054246,0.001692563,0.0007990611,0.001568384,0.001724111,0.002209179,0.005560952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001041173,"about_ca_system_score_gemma":0.001581815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006497054,"about_ca_topic_score_gemma":0.000929149,"domain_scores_codex":[0.9966486,0.0004661467,0.0003021549,0.0006355339,0.001755966,0.000191555],"domain_scores_gemma":[0.9964395,0.001422459,0.0004881294,0.0006261785,0.0008378422,0.0001859447],"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":[0.002842521,0.0003263197,0.01340651,0.002148223,0.0006377597,0.0006261255,0.001018055,0.0139137,0.6540844,0.01486324,0.04993331,0.2461999],"study_design_scores_gemma":[0.0001321677,0.0004580493,0.0135605,0.0001263049,0.0001506583,0.000996602,0.0002341,0.2520645,0.6484835,0.006198231,0.07737118,0.0002241651],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09911185,0.0006843133,0.7479258,0.0007545711,0.0002783305,0.0006368691,0.02195956,0.119023,0.00962576],"genre_scores_gemma":[0.161157,0.0006764822,0.7827082,0.0004195274,0.00008387354,0.001974833,0.02581412,0.02050728,0.00665869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01118584,"threshold_uncertainty_score":0.03742039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03782896299241027,"score_gpt":0.3401843001039058,"score_spread":0.3023553371114955,"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."}}