{"id":"W2755029174","doi":"10.1038/s41598-017-11711-1","title":"Food-grade filler particles as an alternative method to modify the texture and stability of myofibrillar gels","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Muscle metabolism and nutrition","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Starch; Swelling; Food science; Filler (materials); Potato starch; Microcrystalline cellulose; Chemistry; Particle size; Population; Materials science; Chemical engineering; Composite material; Cellulose; Organic chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.001397801,0.00009116359,0.0001270585,0.00002324718,0.0003702882,0.0001676408,0.0001771007,0.00006166774,0.00001441785],"category_scores_gemma":[0.0003105331,0.00006281982,0.00005453014,0.00004510877,0.0002860475,0.00001501633,0.0001710575,0.00003821542,8.45321e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000319241,"about_ca_system_score_gemma":0.00003673969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007619893,"about_ca_topic_score_gemma":0.00005759589,"domain_scores_codex":[0.9987848,0.0001175983,0.0002160021,0.0005168722,0.000208988,0.0001557323],"domain_scores_gemma":[0.9982871,0.00001070184,0.0002172392,0.001233241,0.0001462106,0.0001055211],"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":[0.00003124424,0.00004590108,0.003762647,0.00001417467,0.00001992136,0.000004668578,0.0003156558,0.00001099937,0.9845566,0.0001601407,0.0008074904,0.0102706],"study_design_scores_gemma":[0.0001144118,0.0001393206,0.01797741,0.000007776079,0.00001460562,0.00003428588,0.0001088074,0.00005837308,0.9364612,0.009653933,0.03534095,0.00008898787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969395,0.0003753411,0.001157775,0.0003256303,0.0006725944,0.0002713135,0.00001900497,0.000004448233,0.000234367],"genre_scores_gemma":[0.9975662,0.00001753191,0.002124986,0.00004531987,0.0001170738,0.00001389164,0.00002423103,0.000007054963,0.00008374251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04809542,"threshold_uncertainty_score":0.2847995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03262675890763875,"score_gpt":0.319620428244218,"score_spread":0.2869936693365792,"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."}}