{"id":"W4405730830","doi":"10.1021/acsfoodscitech.4c00896","title":"Automated Beer Analysis by NMR Spectroscopy","year":2024,"lang":"en","type":"article","venue":"ACS Food Science & Technology","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Metabolomics Innovation Centre; University of Alberta","funders":"Canada Foundation for Innovation; National Center for Complementary and Integrative Health; Genome Canada; Alberta Innovates; Office of Dietary Supplements","keywords":"Profiling (computer programming); Nuclear magnetic resonance spectroscopy; Proton NMR; NMR spectra database; Chemistry; Analytical Chemistry (journal); Spectral line; Computer science; Chromatography; Physics; Stereochemistry; Programming language","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":[0.001211759,0.00199546,0.001283086,0.003365981,0.0007655228,0.00202216,0.001350662,0.001124665,0.008685973],"category_scores_gemma":[0.003413614,0.0007374412,0.001263098,0.001748106,0.0003266534,0.00117185,0.002037199,0.001055031,0.006726251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005349411,"about_ca_system_score_gemma":0.0009021367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002039282,"about_ca_topic_score_gemma":0.00261267,"domain_scores_codex":[0.9984596,0.0001749382,0.00008897368,0.000531148,0.0006258367,0.0001194826],"domain_scores_gemma":[0.9986491,0.0003607282,0.0001453617,0.0002028103,0.0005867197,0.00005542364],"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":[0.001149319,0.0001642651,0.007229791,0.001193163,0.0003723214,0.0005770931,0.0002923211,0.005096689,0.743683,0.001216496,0.01302438,0.226001],"study_design_scores_gemma":[0.00009505111,0.0003477067,0.02935286,0.0001882416,0.0002726832,0.001554731,0.0003938368,0.1715341,0.7259659,0.004452127,0.06542067,0.0004221582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1865599,0.003013891,0.7317138,0.0004502166,0.0002985915,0.0006242462,0.01675876,0.05261582,0.007964785],"genre_scores_gemma":[0.2514283,0.002568688,0.7177089,0.0005806645,0.0001807099,0.0006569076,0.01592621,0.005271094,0.005678551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008685973,"threshold_uncertainty_score":0.0290575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006072591873314936,"score_gpt":0.2673018723326534,"score_spread":0.2612292804593385,"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."}}