{"id":"W4411965423","doi":"10.1016/j.afres.2025.101128","title":"Gelatin from red tilapia (Oreochromis spp.) scales: Optimization of its extraction and detailed characterization of its chemical and viscoelastic properties","year":2025,"lang":"en","type":"article","venue":"Applied Food Research","topic":"Collagen: Extraction and Characterization","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Nationale de la Recherche; Providence Health Care; Ministère de l'Enseignement Supérieur et de la Recherche; Université de Lorraine","keywords":"Tilapia; Viscoelasticity; Gelatin; Extraction (chemistry); Characterization (materials science); Oreochromis; Biological system; Biology; Chemistry; Fish <Actinopterygii>; Materials science; Fishery; Chromatography; Nanotechnology; Composite material; Biochemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002087441,0.0003356721,0.0002697213,0.0001943647,0.00009676441,0.0002790039,0.00009414221,0.0001794949,0.0004794519],"category_scores_gemma":[0.0001963739,0.00009340601,0.0002773712,0.0001852833,0.0002071593,0.0002081991,0.0002090355,0.0002718082,0.0001217914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178273,"about_ca_system_score_gemma":0.0002162775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005244953,"about_ca_topic_score_gemma":0.001187224,"domain_scores_codex":[0.9998829,0.0000161289,0.00001500313,0.00003301807,0.0000351187,0.00001767618],"domain_scores_gemma":[0.999925,0.00001791303,0.00002940808,0.00000438385,0.0000122521,0.00001095815],"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":[0.00005180359,0.00001541596,0.0002018949,0.00006141787,0.000003139862,0.00002268077,0.000008439575,0.00008817477,0.9985568,0.000007428937,0.000005415642,0.0009775111],"study_design_scores_gemma":[0.00001332348,0.0007387278,0.01552757,0.0000183848,0.0000407768,0.0001456282,0.00004269185,0.0006766156,0.9813448,0.00003049233,0.001410966,0.000009899299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950109,0.0008054031,0.003401424,0.00003402241,0.000007340671,0.0000618991,0.0001776036,0.00001668905,0.0004847602],"genre_scores_gemma":[0.9861017,0.001012424,0.009801242,0.00005758976,0.000006629733,0.0001417378,0.0005079376,0.00002009314,0.002350587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005244953,"threshold_uncertainty_score":0.001603961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03562537421693871,"score_gpt":0.2914386492780051,"score_spread":0.2558132750610663,"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."}}