{"id":"W2146488171","doi":"10.1371/journal.pone.0124219","title":"Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":301,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; Alberta Innovates; University of Alberta","funders":"Genome Canada; Canadian Institutes of Health Research; Alberta Innovates; Genome Alberta; Natural Sciences and Engineering Research Council of Canada; Pfizer","keywords":"Metabolomics; Nuclear magnetic resonance spectroscopy; Computer science; Profiling (computer programming); Metabolite; Inference; Biological system; NMR spectra database; Proton NMR; Computational biology; Nuclear magnetic resonance; Chemistry; Artificial intelligence; Spectral line; Biology; Physics; Chromatography; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002919376,0.0001919543,0.0003332059,0.00005961848,0.00007943052,0.00003616087,0.0001919904,0.0001216099,0.000007516345],"category_scores_gemma":[0.0004431702,0.0001785549,0.00009388271,0.000127755,0.00004854917,0.000005723618,0.0001161823,0.00007845224,0.00001572795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001981475,"about_ca_system_score_gemma":0.00009388648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004815502,"about_ca_topic_score_gemma":0.00001026087,"domain_scores_codex":[0.9988088,0.00003147571,0.0002451058,0.000371421,0.0001686495,0.0003745609],"domain_scores_gemma":[0.9991803,0.0000156359,0.0001056505,0.0003062865,0.0002603902,0.0001317851],"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":[0.0002204298,0.00051149,0.001379559,0.00005120624,0.0007387407,0.000001263043,0.00003043423,0.00003626353,0.9920864,0.001545537,0.003339945,0.00005880558],"study_design_scores_gemma":[0.001289899,0.0004916337,0.0003162863,0.00001021792,0.000212126,0.000002932057,0.0001185181,0.002849099,0.9875151,0.0005332691,0.006355802,0.0003051119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932899,0.00237855,0.001059058,0.0002820806,0.0001259752,0.0005725669,0.00009585972,0.0001115961,0.002084381],"genre_scores_gemma":[0.938144,0.0005391107,0.05863269,0.0002291037,0.0006738167,0.0001539828,0.00030572,0.0000492183,0.001272319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05757364,"threshold_uncertainty_score":0.7281259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06633721686489223,"score_gpt":0.2768799156012077,"score_spread":0.2105426987363154,"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."}}