{"id":"W2468323900","doi":"10.1557/opl.2016.70","title":"Arborescent Polypeptides for Sustained Drug Delivery","year":2016,"lang":"en","type":"article","venue":"MRS Proceedings","topic":"Biopolymer Synthesis and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Taibah University","keywords":"Grafting; Materials science; Yield (engineering); Substrate (aquarium); Ethylene oxide; Micelle; Solvent; Coupling reaction; Amine gas treating; Chemical engineering; Oxide; Drug delivery; Size-exclusion chromatography; Mole fraction; Polymer chemistry; Organic chemistry; Nanotechnology; Copolymer; Catalysis; Composite material; Physical chemistry; Chemistry; Polymer; Aqueous solution","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":[0.000228221,0.0003024655,0.0001417163,0.000198086,0.0001491628,0.0003375637,0.0001753027,0.0002554257,0.001863306],"category_scores_gemma":[0.0001528727,0.000110347,0.0001360646,0.0001338849,0.0001613494,0.0003440385,0.0002380453,0.0004657866,0.0008350994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003215781,"about_ca_system_score_gemma":0.0001462771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001893305,"about_ca_topic_score_gemma":0.0005281722,"domain_scores_codex":[0.9999343,0.00001355964,0.000005191893,0.00001273165,0.00001964362,0.00001448441],"domain_scores_gemma":[0.9999334,0.00001517479,0.0000168334,0.00000586649,0.00001105889,0.00001769077],"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":[0.00002094535,0.00001134475,0.0000274866,0.00004451062,0.000002825784,0.0000397046,0.00001378467,0.0001059292,0.9926062,0.0008522366,0.0002008344,0.006074285],"study_design_scores_gemma":[0.00001045143,0.0001668494,0.0004344068,0.00001009239,0.00001192449,0.0001457137,0.00001398623,0.001185603,0.9821291,0.0002758518,0.01560877,0.000007358931],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8440498,0.02697965,0.09596098,0.001242568,0.000572523,0.0001854003,0.0005059504,0.0009633481,0.0295398],"genre_scores_gemma":[0.9291524,0.01287813,0.03364656,0.0003297846,0.0001717603,0.0001301878,0.0003736749,0.0001196306,0.02319779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001863306,"threshold_uncertainty_score":0.006233335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006846076037626762,"score_gpt":0.2243753096236848,"score_spread":0.2175292335860581,"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."}}