{"id":"W2995792875","doi":"10.1002/aocs.12303","title":"Nuclear Magnetic Resonance Spectroscopy: A Versatile Tool for Qualitative and Quantitative Analysis of an Emulsifier Mixture of Soybean Oil","year":2019,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Medical Council of Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Chemistry; Polyunsaturated fatty acid; Nuclear magnetic resonance spectroscopy; Soybean oil; Spectroscopy; Fatty acid; Quantitative analysis (chemistry); Organic chemistry; Chromatography; Qualitative analysis; Vegetable oil; Oil analysis; Methyl oleate; Food science; Materials science","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":[0.0009820262,0.0009340761,0.0002962369,0.001159212,0.000228266,0.0003106585,0.0003189876,0.0005169156,0.0009260103],"category_scores_gemma":[0.000586774,0.0003454209,0.0002218287,0.000506495,0.0004340486,0.0005196757,0.0004292095,0.0007056443,0.00035827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002165783,"about_ca_system_score_gemma":0.0002511351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000358429,"about_ca_topic_score_gemma":0.0006762462,"domain_scores_codex":[0.9993744,0.0001787481,0.00003207088,0.000139862,0.0002391599,0.00003584093],"domain_scores_gemma":[0.9996867,0.00009190531,0.00007551429,0.00003240219,0.00008595732,0.00002753874],"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":[0.00002066486,0.000003906595,0.00007496829,0.00003156197,0.000004221918,0.00001323004,0.000006315862,0.00003616976,0.9977579,0.00002563979,0.00001129931,0.002014229],"study_design_scores_gemma":[0.000004664272,0.000113461,0.001569113,0.00001098908,0.00002013578,0.0001276181,0.00002394574,0.001629666,0.9948509,0.0001022575,0.00153288,0.00001434968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5045498,0.0105588,0.4775588,0.0002833639,0.0001579208,0.0001732793,0.001157327,0.001118525,0.004442211],"genre_scores_gemma":[0.7517688,0.005344238,0.2376364,0.0002085227,0.00006394089,0.0002918907,0.0007391081,0.0002063721,0.003740843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001159212,"threshold_uncertainty_score":0.005193532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008474008529427448,"score_gpt":0.2918663896481144,"score_spread":0.283392381118687,"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."}}