{"id":"W4393119011","doi":"10.1002/pca.3348","title":"Discriminating extra virgin olive oils from common edible oils: Comparable performance of PLS‐DA models trained on low‐field and high‐field <sup>1</sup> H NMR data","year":2024,"lang":"en","type":"article","venue":"Phytochemical Analysis","topic":"Edible Oils Quality and Analysis","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; The Metabolomics Innovation Centre; Okanagan University College; University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Genome Alberta; Canada Foundation for Innovation; Genome Canada","keywords":"Chemistry; Olive oil; Partial least squares regression; Olea; NMR spectra database; Proton NMR; Edible oil; Adulterant; Chemometrics; Pulp and paper industry; Food science; Chromatography; Spectral line; Machine learning; Botany; Organic chemistry; Computer 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003907016,0.0004407474,0.001175782,0.000288808,0.0001611709,0.0001951559,0.0008865711,0.0002945524,0.001317263],"category_scores_gemma":[0.0001751784,0.0003993079,0.0004190849,0.001215084,0.0001569693,0.0005588964,0.0003771552,0.0007573334,0.00001703036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005341951,"about_ca_system_score_gemma":0.00004486975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002722976,"about_ca_topic_score_gemma":0.00009342459,"domain_scores_codex":[0.9967387,0.00007058803,0.0009192647,0.001111837,0.0006594533,0.0005001093],"domain_scores_gemma":[0.9964595,0.001669555,0.0002118156,0.00132818,0.00008555043,0.0002454151],"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":[0.001372283,0.002407482,0.00793176,0.01690318,0.01896912,0.0001420431,0.009498577,0.08562033,0.8006209,0.004510289,0.006325993,0.04569809],"study_design_scores_gemma":[0.0002932179,0.00004238999,0.000005433007,0.000828852,0.002349548,9.910812e-7,0.0006371856,0.6424243,0.3510529,0.002000424,0.00002513577,0.0003396093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879111,0.001753332,0.005698969,0.0004892712,0.00002318766,0.00005432828,0.0006615919,0.000122179,0.00328602],"genre_scores_gemma":[0.9954818,0.0008866765,0.001263169,0.0002402054,0.0003913607,0.00002649452,0.001457301,0.00003439794,0.0002186144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.556804,"threshold_uncertainty_score":0.9998459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05038171572440564,"score_gpt":0.2983485625935318,"score_spread":0.2479668468691262,"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."}}