{"id":"W4386979916","doi":"10.3390/gels9090766","title":"Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility Properties","year":2023,"lang":"en","type":"article","venue":"Gels","topic":"Protein Hydrolysis and Bioactive Peptides","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Agencia Nacional de Investigación y Desarrollo","keywords":"Gelatin; Response surface methodology; Nozzle; Extrusion; Materials science; Chemical engineering; Artificial neural network; Biological system; Hydrolysis; 3D printing; Composite material; Chromatography; Computer science; Chemistry; Mechanical engineering; Biochemistry; Artificial intelligence","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":[0.0008164542,0.0001547127,0.0001808027,0.00002676134,0.0001585784,0.00004337513,0.00005812072,0.0001523197,0.000001042966],"category_scores_gemma":[0.0007357068,0.0001282555,0.00005010005,0.00008888142,0.0001817119,0.00001222206,0.0001509369,0.00009933152,3.030596e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001288379,"about_ca_system_score_gemma":0.0000228006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000304794,"about_ca_topic_score_gemma":0.000006395864,"domain_scores_codex":[0.9988145,0.0001921358,0.000200313,0.000465017,0.00008572893,0.0002423118],"domain_scores_gemma":[0.9993315,0.0002738808,0.00009246542,0.0001728414,0.00007855878,0.00005080738],"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":[0.0009512284,0.00001569201,0.002059167,0.00001095865,0.00001918444,5.577059e-7,0.00001790832,0.2982264,0.69699,0.000006641328,0.000002911692,0.001699368],"study_design_scores_gemma":[0.0001581264,0.0001822851,0.006863872,0.00002179239,0.00001767932,0.000004250986,0.00001174488,0.4911571,0.5011905,0.0002485354,0.000006437576,0.0001375645],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838173,0.0001571478,0.01542533,0.0001299491,0.00003446683,0.0004014156,0.00001112691,0.00002245056,8.318472e-7],"genre_scores_gemma":[0.9839062,0.000007414375,0.01583582,0.00008764862,0.00009084007,0.00002196263,0.0000274237,0.0000163296,0.000006344421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1957994,"threshold_uncertainty_score":0.5230106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08200820618783039,"score_gpt":0.3108683754711076,"score_spread":0.2288601692832772,"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."}}