{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003469326,0.0001830528,0.000247706,0.001181985,0.0002248076,0.00009843441,0.0006687527,0.0001880997,0.00002351318],"category_scores_gemma":[0.0001447713,0.0001588982,0.0001028364,0.006019029,0.0008657604,0.00001024634,0.0003887788,0.0001571142,0.00003095497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004073313,"about_ca_system_score_gemma":0.0001281261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007573843,"about_ca_topic_score_gemma":0.00002958481,"domain_scores_codex":[0.9982324,0.00001253631,0.0002024138,0.0007898721,0.0002241459,0.0005385981],"domain_scores_gemma":[0.9992254,0.000007716528,0.00004663914,0.0005591735,0.00009311343,0.00006792087],"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":[0.000003621446,0.00002499571,0.000976229,0.000004065606,0.0003309787,0.00000295857,0.00001298091,0.00001067929,0.9859979,0.006352483,0.005760134,0.0005229567],"study_design_scores_gemma":[0.00009191829,0.0005206728,0.0003629756,0.000003189353,0.0001586639,0.00001323903,0.00008007634,0.0007030221,0.9274735,0.001189794,0.06918924,0.0002137277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828715,0.0088188,0.003392259,0.00209126,0.0002489139,0.0001220343,0.0000452861,0.0004980381,0.001911886],"genre_scores_gemma":[0.9972739,0.0005909757,0.001459843,0.0001301927,0.0000470987,0.00002756124,0.00002704408,0.00001465191,0.0004287026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0634291,"threshold_uncertainty_score":0.6479682,"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."}}