{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001680888,0.000220525,0.00008350312,0.0001554097,0.00009825921,0.0001666642,0.0001152786,0.0001607716,0.0007959207],"category_scores_gemma":[0.0001445776,0.00009731611,0.0001898164,0.000105214,0.00009617337,0.0002409259,0.0001503505,0.0002091871,0.000166105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001640143,"about_ca_system_score_gemma":0.0001075676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004424664,"about_ca_topic_score_gemma":0.00113265,"domain_scores_codex":[0.999921,0.000008929696,0.000006770704,0.00001353012,0.00002149547,0.00002835549],"domain_scores_gemma":[0.9999082,0.00001903011,0.0000223053,0.000009996653,0.00002030179,0.00002021838],"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":[0.00002582495,0.000008065395,0.00006943426,0.00002586295,0.000002291513,0.00002174106,0.00001502359,0.0001229574,0.9986614,0.00004702366,0.00001436125,0.0009859919],"study_design_scores_gemma":[0.000001641628,0.00003348561,0.0005858475,0.000001629853,0.000003277644,0.00002253597,0.00001112025,0.0003764899,0.9984772,0.00001064252,0.0004732071,0.000002870551],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938574,0.0005996045,0.003957459,0.00002928091,0.00001142073,0.00001511493,0.0001095086,0.00002657624,0.001393616],"genre_scores_gemma":[0.9957389,0.0003196663,0.00202578,0.00001712416,0.00000258868,0.00001104874,0.00008491238,0.00001162862,0.001788349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007959207,"threshold_uncertainty_score":0.002662659,"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."}}