{"id":"W4405401261","doi":"10.1016/j.foodhyd.2024.110990","title":"Hydrogel structure of soy protein and gelatin dual network based on TGase cross-linking","year":2024,"lang":"en","type":"article","venue":"Food Hydrocolloids","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China","keywords":"Gelatin; Soy protein; Chemistry; Dual (grammatical number); Network structure; Food science; Chemical engineering; Biochemistry; Computer science","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":[0.00005959362,0.0001806398,0.00008463766,0.0001251073,0.00009914914,0.0001168254,0.0001819508,0.0001977408,0.001004489],"category_scores_gemma":[0.00009651191,0.0001157113,0.0001495226,0.00008502061,0.0001604197,0.0002643679,0.0001237418,0.0002825487,0.0001262419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001532755,"about_ca_system_score_gemma":0.00009446838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000371785,"about_ca_topic_score_gemma":0.0007071472,"domain_scores_codex":[0.9999521,0.000004003891,0.000002687058,0.00001666586,0.000012711,0.00001171094],"domain_scores_gemma":[0.9999336,0.000009382005,0.00002470806,0.000004453246,0.00000866521,0.00001918121],"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":[0.00003315839,0.000005941259,0.00004952851,0.00002306954,0.000002524072,0.00003770242,0.000009547785,0.0001380318,0.9991373,0.00008592081,0.00001725807,0.0004600758],"study_design_scores_gemma":[0.000009699944,0.00012513,0.001498618,0.00000461236,0.000009982041,0.00008505369,0.00001335704,0.002777816,0.9947779,0.00003523836,0.0006563273,0.000006136727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934286,0.000513763,0.004579428,0.00006158803,0.00002361247,0.00001089005,0.0001146894,0.00005717754,0.001210251],"genre_scores_gemma":[0.9959876,0.0001786752,0.002535916,0.00002632121,0.000004077415,0.00001015228,0.00008093444,0.000008966913,0.001167358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001004489,"threshold_uncertainty_score":0.003360331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007098682865834155,"score_gpt":0.297074467833477,"score_spread":0.2899757849676429,"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."}}