{"id":"W2937662637","doi":"10.3390/metabo9040072","title":"CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification","year":2019,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":264,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Genome Alberta; Genome Canada","keywords":"Identification (biology); Computer science; Metadata; Mass spectrometry; Tandem mass spectrometry; Pattern recognition (psychology); Chemistry; Artificial intelligence; Chromatography; Biology","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":[0.005519527,0.003014519,0.001777049,0.00337326,0.0007546454,0.001903583,0.005583324,0.002457611,0.006713637],"category_scores_gemma":[0.008171063,0.001452723,0.002588846,0.001999685,0.0005023202,0.002829005,0.002858887,0.003138068,0.004645679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001674486,"about_ca_system_score_gemma":0.003770515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009653107,"about_ca_topic_score_gemma":0.009747673,"domain_scores_codex":[0.9976451,0.0002716438,0.0001573778,0.0005464571,0.001171204,0.0002082811],"domain_scores_gemma":[0.9966089,0.001222977,0.000241216,0.0006971933,0.001027788,0.0002020695],"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":[0.00331647,0.001148901,0.01658966,0.001145927,0.0008539726,0.0007202666,0.0001827612,0.09986948,0.09946516,0.008672532,0.1355476,0.6324873],"study_design_scores_gemma":[0.0002085629,0.0001845079,0.001816291,0.00004002906,0.0000764787,0.0002527577,0.00001375574,0.9075258,0.06265581,0.003342471,0.0237293,0.0001541549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03533043,0.001162778,0.6871879,0.0007245496,0.000458294,0.0003614109,0.00978728,0.2601489,0.004838475],"genre_scores_gemma":[0.06877611,0.0003353475,0.9052636,0.0007654689,0.0000988033,0.0003183018,0.01701588,0.00473978,0.002686824],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009653107,"threshold_uncertainty_score":0.02919036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006506425331549946,"score_gpt":0.2214065229568456,"score_spread":0.2149000976252956,"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."}}