{"id":"W4413431909","doi":"10.1016/b978-0-443-21736-4.00004-0","title":"Sericin-based nanospheres and other nanoformulations for various applications","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Silk-based biomaterials and applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sericin; Materials science; SILK; Composite material","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":[0.00009763828,0.0004148445,0.0001468196,0.0002538525,0.0001081744,0.0003561481,0.000201869,0.0002881984,0.00300019],"category_scores_gemma":[0.00003400675,0.0001718828,0.0002356022,0.0002833877,0.000147524,0.0006024552,0.0002224314,0.0005215321,0.001163457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002364837,"about_ca_system_score_gemma":0.00009088287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000243974,"about_ca_topic_score_gemma":0.0006158301,"domain_scores_codex":[0.9999745,0.000001254248,0.000001350994,0.00000589579,0.00001192289,0.000005169572],"domain_scores_gemma":[0.9999894,0.000003451955,0.000001591355,0.000001558823,0.000001791309,0.000002242279],"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":[0.00003020604,0.00005558613,0.00002923295,0.0003189482,0.000007545275,0.00008284402,0.00004414974,0.0005700135,0.9161862,0.003541486,0.002701221,0.07643263],"study_design_scores_gemma":[0.00001072779,0.0001801774,0.0007811211,0.00005620538,0.00002035795,0.0003794036,0.00003319414,0.001599587,0.8226122,0.001865476,0.1724426,0.00001891961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2858996,0.2882922,0.100837,0.001303369,0.002389213,0.0001969575,0.0009799864,0.001986786,0.3181148],"genre_scores_gemma":[0.4576886,0.14562,0.03895084,0.001042703,0.0004324834,0.0001059035,0.0009691655,0.00039123,0.3547991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00300019,"threshold_uncertainty_score":0.01003665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443517784108845,"score_gpt":0.2561268967934132,"score_spread":0.2416917189523248,"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."}}