{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000928156,0.0001036914,0.00007858743,0.0000190168,0.00008659677,0.00002172167,0.0001536901,0.00006539094,0.00001242394],"category_scores_gemma":[0.00004424932,0.00007175835,0.00007466566,0.00004327954,0.00006289246,0.000004335684,0.00005398861,0.00001854184,0.000009036826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001368349,"about_ca_system_score_gemma":0.00003137592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006491019,"about_ca_topic_score_gemma":0.00000398647,"domain_scores_codex":[0.9993091,0.000001830748,0.0001193456,0.0002683695,0.00006107789,0.00024025],"domain_scores_gemma":[0.9996387,0.000009235563,0.00005195224,0.000102888,0.0001256937,0.00007146872],"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":[0.00004784613,0.00003063254,0.001513726,0.00001423164,0.00001721062,5.464851e-8,0.00001748406,9.357834e-9,0.9574831,0.002312716,0.02801186,0.01055113],"study_design_scores_gemma":[0.0002799855,0.0000470422,0.000616303,0.00001446996,0.00001150292,0.000001325716,0.00009025716,0.000003758138,0.8443409,0.0002931833,0.1541801,0.000121151],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928392,0.0004883612,0.0008502458,0.003966395,0.00004652615,0.0003071067,0.00003216612,0.00003256679,0.001437414],"genre_scores_gemma":[0.9951894,0.0001361288,0.0008777867,0.0002855205,0.0002972447,0.0001622666,0.000008688471,0.00002136991,0.00302162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1261682,"threshold_uncertainty_score":0.2926221,"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."}}