{"id":"W3090196112","doi":"10.3390/membranes10100268","title":"Bovine Hemoglobin Enzymatic Hydrolysis by a New Eco-Efficient Process-Part II: Production of Bioactive Peptides","year":2020,"lang":"en","type":"article","venue":"Membranes","topic":"Protein Hydrolysis and Bioactive Peptides","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Région Hauts-de-France; Agence Nationale de la Recherche; Université Laval","keywords":"Chemistry; Enzymatic hydrolysis; Hydrolysate; Hydrolysis; Hemoglobin; Antioxidant; Chromatography; Context (archaeology); Electrodialysis; Biochemistry; Membrane; Food science; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001818873,0.0003763197,0.0001327767,0.0002177048,0.00008742701,0.0002529925,0.0001722213,0.0003124037,0.0003233052],"category_scores_gemma":[0.000123469,0.0001095933,0.0001874103,0.0001820384,0.0001381704,0.0002035034,0.0002047069,0.0003832794,0.0001718902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001553983,"about_ca_system_score_gemma":0.0001521398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002886802,"about_ca_topic_score_gemma":0.0003798003,"domain_scores_codex":[0.9998562,0.00002145685,0.00001312711,0.00003820692,0.00005211019,0.00001889307],"domain_scores_gemma":[0.9999377,0.00000998618,0.0000242565,0.000005657812,0.00001260922,0.000009874206],"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":[0.00001061161,0.00000666587,0.00004836202,0.0000147233,0.000001411864,0.00001194817,0.00000423561,0.00001180568,0.9988243,0.00001634474,0.000005218223,0.001044328],"study_design_scores_gemma":[0.000005011097,0.00009153804,0.001262585,0.0000041195,0.000007486291,0.0001629904,0.000007126322,0.0001895991,0.9970098,0.00001145164,0.001245604,0.000002729713],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279453,0.004864991,0.06473228,0.0001680146,0.00005177269,0.00009799633,0.0002591141,0.0001036371,0.001776866],"genre_scores_gemma":[0.9606237,0.002889532,0.0327171,0.0001049365,0.00001864443,0.00006540589,0.000499553,0.00003469585,0.003046438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0003763197,"threshold_uncertainty_score":0.001127541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01037139747604058,"score_gpt":0.2287638448501373,"score_spread":0.2183924473740968,"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."}}