{"id":"W4413897127","doi":"10.1002/nbm.70131","title":"Automatic Identification of Potential Cellular Metabolites for Untargeted NMR Metabolomics","year":2025,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Cancer Institute; National Institute on Aging; Canadian Institutes of Health Research","keywords":"Metabolomics; Metabolite; Metabolome; Proton NMR; Chemistry; Nuclear magnetic resonance spectroscopy; Computational biology; Phosphocholine; Nuclear magnetic resonance; Biochemistry; Bioinformatics; Biology; Chromatography","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":[0.000819618,0.001379778,0.000921061,0.001932241,0.0005467401,0.001234704,0.0008874992,0.0005626499,0.005004005],"category_scores_gemma":[0.00185311,0.0004348719,0.001061232,0.001337085,0.0003438313,0.0009344599,0.001291044,0.0005868529,0.00223249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005543166,"about_ca_system_score_gemma":0.00114749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001459129,"about_ca_topic_score_gemma":0.00229459,"domain_scores_codex":[0.9996693,0.00003762211,0.00002326534,0.0001558281,0.00007698919,0.00003701701],"domain_scores_gemma":[0.9994373,0.0002413871,0.00009547616,0.00008857165,0.00009596728,0.00004144992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002863862,0.0004915927,0.03266601,0.00234738,0.0005423282,0.00113667,0.0005160035,0.02117279,0.4657418,0.003714734,0.03619313,0.4326137],"study_design_scores_gemma":[0.0002609734,0.0006424934,0.0384254,0.0001288957,0.0003280068,0.001252054,0.0003101294,0.5222965,0.3748484,0.01406684,0.04725112,0.0001892346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1379088,0.001130393,0.6654521,0.0004452277,0.0001416392,0.0003704259,0.03342874,0.1578117,0.003310965],"genre_scores_gemma":[0.276746,0.0008316058,0.682027,0.0003525417,0.00006572156,0.0009899079,0.0311657,0.005014302,0.002807181],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005004005,"threshold_uncertainty_score":0.01674008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006757076115081797,"score_gpt":0.2676692347983049,"score_spread":0.2609121586832231,"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."}}