{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002959653,0.0005890797,0.0004532911,0.0003390106,0.000241346,0.0002702554,0.0002368696,0.0002495811,0.0004322553],"category_scores_gemma":[0.0002592344,0.00009316316,0.0003690786,0.0004087014,0.0001833757,0.0002308168,0.0001937354,0.0002788124,0.0002581895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000394801,"about_ca_system_score_gemma":0.0003655989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00195268,"about_ca_topic_score_gemma":0.003709554,"domain_scores_codex":[0.999804,0.00003627845,0.00002394655,0.00004485698,0.00006364693,0.00002731276],"domain_scores_gemma":[0.9998791,0.00001779076,0.00004100084,0.000007382111,0.0000351113,0.00001956902],"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":[0.0001074827,0.00001532429,0.0005492831,0.00009741151,0.000009353052,0.00005401886,0.00001362716,0.00005053339,0.9973518,0.0000132084,0.00002881363,0.001709114],"study_design_scores_gemma":[0.000008340432,0.0002410549,0.01414933,0.00002063271,0.00005276153,0.0005500951,0.00004570239,0.0003884692,0.9814035,0.00002350761,0.003106129,0.0000104812],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903405,0.002788702,0.004886346,0.00009448614,0.0000175193,0.0000669984,0.0006304139,0.00006350169,0.001111623],"genre_scores_gemma":[0.9615813,0.002432384,0.02974944,0.0001254207,0.0000167986,0.000103566,0.00282841,0.00007717927,0.003085564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00195268,"threshold_uncertainty_score":0.003882706,"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."}}