{"id":"W3194841450","doi":"10.1021/acs.analchem.1c01465","title":"CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification","year":2021,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":396,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Eesti Teadusagentuur; Genome British Columbia; Canada Foundation for Innovation; Alberta Machine Intelligence Institute; National Institute of Environmental Health Sciences; Canadian Institutes of Health Research; Genome Canada; Canadian Institute for Advanced Research","keywords":"Chemistry; Identification (biology); Mass spectrometry; Chromatography; Analytical Chemistry (journal)","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":[0.005931232,0.003053209,0.001744117,0.003584991,0.0007929187,0.002530225,0.004294895,0.002770025,0.01771202],"category_scores_gemma":[0.01393586,0.001265545,0.002759853,0.001599338,0.0005123177,0.002976246,0.002763053,0.002831356,0.009857849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001796843,"about_ca_system_score_gemma":0.003114477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0058469,"about_ca_topic_score_gemma":0.007164016,"domain_scores_codex":[0.9981012,0.0002294351,0.0001423155,0.0005510589,0.0008218494,0.0001542458],"domain_scores_gemma":[0.995641,0.001967586,0.0003480006,0.0008610798,0.000968139,0.0002141471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003700082,0.001071561,0.03005824,0.002618311,0.001842952,0.0008499818,0.0002304675,0.08628226,0.05765408,0.007974399,0.3168593,0.4908584],"study_design_scores_gemma":[0.0003465996,0.0003102188,0.00412587,0.0001384256,0.0001717177,0.0007304907,0.00003355501,0.8519734,0.06763152,0.006680069,0.06762546,0.0002326407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03006866,0.001468971,0.565882,0.0008491593,0.0007649565,0.0005587664,0.03413535,0.3592162,0.007056007],"genre_scores_gemma":[0.1004571,0.0005044295,0.8177128,0.00117344,0.0001435662,0.0006090088,0.06172001,0.01248776,0.005191812],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01771202,"threshold_uncertainty_score":0.05925262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203279389417765,"score_gpt":0.2641669390528795,"score_spread":0.2521341451587019,"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."}}