{"id":"W2549578387","doi":"10.1007/s12649-016-9773-0","title":"Synthesis and Properties of Feather Keratin-Based Superabsorbent Hydrogels","year":2016,"lang":"en","type":"article","venue":"Waste and Biomass Valorization","topic":"Dyeing and Modifying Textile Fibers","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Self-healing hydrogels; Swelling; Potassium persulfate; Differential scanning calorimetry; Polymer chemistry; Acrylic acid; Acrylamide; Sodium bisulfite; Copolymer; Monomer; Fourier transform infrared spectroscopy; Chemical engineering; Chemistry; Nuclear chemistry; Distilled water; Ammonium persulfate; Keratin; Materials science; Polymer; Organic chemistry; Chromatography; Composite material","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":[0.00006115195,0.00008591406,0.00009906395,0.00006090865,0.000032833,0.00001684381,0.00002804643,0.00004526211,0.000006197883],"category_scores_gemma":[0.00001990806,0.0000549098,0.00001701401,0.0000550406,0.00005060122,0.00007093657,0.000008336257,0.00001341797,0.000001833168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001147922,"about_ca_system_score_gemma":0.000007277843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008197362,"about_ca_topic_score_gemma":0.000002459554,"domain_scores_codex":[0.9996051,0.00001473225,0.0001127598,0.0001010241,0.0000732109,0.00009314984],"domain_scores_gemma":[0.999822,0.00002476622,0.00001725561,0.00008209566,0.00002104627,0.00003284611],"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":[0.000008461162,0.000008978035,0.00113205,0.0001525858,0.00001818286,3.981808e-7,0.0001498024,0.000147031,0.9757897,0.00008572581,0.0000403776,0.02246667],"study_design_scores_gemma":[0.0002469779,0.00002950548,0.0002579964,0.0002173958,0.00001968032,0.000001139556,0.00003808612,0.00964056,0.9890967,0.00002436442,0.0003219032,0.0001056736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931496,0.000212344,0.006125115,0.00009557712,0.00007817416,0.00007543402,0.000008480905,0.00009059902,0.0001647183],"genre_scores_gemma":[0.9995456,0.00006608664,0.0002425493,0.000005662301,0.00003008533,0.00001148607,0.00000167093,0.0000170284,0.00007985727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02236099,"threshold_uncertainty_score":0.2239157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01549490026558676,"score_gpt":0.1833883551504606,"score_spread":0.1678934548848738,"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."}}