{"id":"W3005703146","doi":"10.1002/pmic.201900352","title":"Machine Learning in Mass Spectrometric Analysis of DIA Data","year":2020,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mass spectrometry; Chromatography; Computer science; Chemistry","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":[0.005374021,0.000838322,0.0008777396,0.002080225,0.0004730086,0.001815798,0.001498938,0.001154863,0.00131997],"category_scores_gemma":[0.01363753,0.000586198,0.0006389338,0.002018648,0.001154431,0.001853495,0.001528953,0.002373248,0.001251229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275574,"about_ca_system_score_gemma":0.001276499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002759084,"about_ca_topic_score_gemma":0.002729924,"domain_scores_codex":[0.9981648,0.0007650107,0.0001293876,0.0003914806,0.0004448538,0.0001044343],"domain_scores_gemma":[0.9933775,0.004586753,0.0004413497,0.0006322555,0.000807793,0.0001544338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002604876,0.0003684635,0.009485123,0.0005639019,0.000216196,0.000274225,0.0001757083,0.2926165,0.00908321,0.02914367,0.01045289,0.6473597],"study_design_scores_gemma":[0.000006479609,0.00002625696,0.0007960157,0.00002014228,0.000006924979,0.00004995266,0.00001703729,0.9715741,0.002610125,0.02280082,0.002079943,0.00001219668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01663041,0.001885915,0.9763515,0.001565012,0.0001118637,0.00008676066,0.0002424031,0.001578187,0.001547829],"genre_scores_gemma":[0.3211603,0.00252561,0.6694671,0.0009869244,0.0003684946,0.0003091896,0.001171694,0.0002941511,0.003716494],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005374021,"threshold_uncertainty_score":0.02842087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03698807444250093,"score_gpt":0.2784381170912524,"score_spread":0.2414500426487515,"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."}}