{"id":"W1915717499","doi":"10.1002/jsfa.7056","title":"Fenugreek (<i>Trigonella foenum graecum</i>) seed protein isolate: extraction optimization, amino acid composition, thermo and functional properties","year":2014,"lang":"en","type":"article","venue":"Journal of the Science of Food and Agriculture","topic":"Proteins in Food Systems","field":"Agricultural and Biological Sciences","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Ferdowsi University of Mashhad","keywords":"Trigonella; Extraction (chemistry); Chemistry; Protein isolate; Solubility; Differential scanning calorimetry; Soy protein; Denaturation (fissile materials); Plant protein; Protein purification; Emulsion; Amino acid; Food science; Chromatography; Biochemistry; Botany; Biology; Nuclear chemistry; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007147837,0.0001325655,0.0001985441,0.00002516943,0.0005367395,0.0001397478,0.0003087837,0.00007001411,0.000007849079],"category_scores_gemma":[0.00008922184,0.00003697618,0.00007915508,0.0004335365,0.0003469245,0.0004849476,0.00008323422,0.0001893343,5.616914e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002399007,"about_ca_system_score_gemma":0.00002130292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001244367,"about_ca_topic_score_gemma":0.00001319972,"domain_scores_codex":[0.9986551,0.0001215435,0.0003494157,0.0001814935,0.0005225413,0.0001698785],"domain_scores_gemma":[0.9987674,0.00002771348,0.0005679867,0.00005638517,0.0004854468,0.00009510079],"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":[0.00003465591,0.00004808181,0.0002600071,0.00001417099,0.00001194229,1.947117e-7,0.00008381677,0.0004265595,0.9979137,0.0001398493,0.00008758486,0.0009794239],"study_design_scores_gemma":[0.0003711956,0.001290837,0.08887707,0.000304326,0.00003617095,0.0004621801,0.0005255647,0.0005711587,0.906349,0.0007636678,0.0002389408,0.0002099105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964647,0.0006454424,0.0000997826,0.002127009,0.0002502638,0.0002728199,0.000005727753,0.000009527577,0.000124739],"genre_scores_gemma":[0.9992854,0.00001953681,0.0002742898,0.00004103366,0.0002810576,0.000004403407,9.442147e-7,8.519463e-7,0.00009246269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09156473,"threshold_uncertainty_score":0.4128221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01340122248857094,"score_gpt":0.1811142544228675,"score_spread":0.1677130319342966,"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."}}