{"id":"W2088907937","doi":"10.1007/s11746-008-1238-6","title":"Protein Subunit Composition Effects on the Thermal Denaturation at Different Stages During the Soy Protein Isolate Processing and Gelation Profiles of Soy Protein Isolates","year":2008,"lang":"en","type":"article","venue":"Journal of the American Oil Chemists Society","topic":"Proteins in Food Systems","field":"Agricultural and Biological Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada; Ministry of Agriculture, Food and Rural Affairs","funders":"","keywords":"Soy protein; Denaturation (fissile materials); Differential scanning calorimetry; Protein subunit; Protein isolate; Pea protein; Chemistry; Composition (language); Rheology; Soybean Proteins; Chromatography; Food science; Biochemistry; Materials science; Nuclear 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":[0.0004835254,0.0002329701,0.0003204892,0.000008126273,0.001100246,0.00007368228,0.0004506513,0.00008406434,0.000006402598],"category_scores_gemma":[0.00009111597,0.00006676705,0.0002532974,0.000252656,0.0005055916,0.0001688335,0.0001489503,0.0005048725,7.590786e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001700975,"about_ca_system_score_gemma":0.00002366601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004624872,"about_ca_topic_score_gemma":0.00000740342,"domain_scores_codex":[0.9981853,0.000382827,0.0004553859,0.0001997385,0.0005316366,0.0002450729],"domain_scores_gemma":[0.9977946,0.0001710628,0.001653174,0.0001328514,0.0001947843,0.00005357126],"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":[0.0001840351,0.00006626827,0.002426632,0.0001445995,0.00005336029,0.000001083398,0.0009319579,0.00001500154,0.9918967,0.000005334083,0.00001551128,0.004259515],"study_design_scores_gemma":[0.000218488,0.0003190477,0.07241694,0.0006008644,0.00002002684,0.00005098543,0.0004054084,0.0002506972,0.925512,0.00004239825,0.00002413027,0.0001389903],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953365,0.0003159835,0.00000304384,0.003453921,0.00002303166,0.0008026247,0.000006353148,0.00002036474,0.00003812343],"genre_scores_gemma":[0.9991358,0.00002869616,0.00007163183,0.00006983623,0.0002574571,0.000097426,0.000002106459,0.000005084065,0.0003319754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06999031,"threshold_uncertainty_score":0.8462314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01049417705762145,"score_gpt":0.202915412552647,"score_spread":0.1924212354950256,"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."}}