{"id":"W4308774833","doi":"10.1002/adma.202208824","title":"Bionic Engineered Protein Coating Boosting Anti‐Biofouling in Complex Biological Fluids","year":2022,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Polymer Surface Interaction Studies","field":"Materials Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; China Scholarship Council; Canada Foundation for Innovation","keywords":"Biofouling; Materials science; Boosting (machine learning); Coating; Nanotechnology; Polymer science; Chemical engineering; Engineering; Chemistry; Computer science; Biochemistry","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":[0.0001245219,0.0003824395,0.0001493849,0.0001553856,0.00010064,0.0002063546,0.0001583796,0.0002774064,0.0003774662],"category_scores_gemma":[0.0001244864,0.0001053701,0.0001272817,0.0001310598,0.0001726168,0.0002184973,0.0002001365,0.0002193486,0.0001440393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001535811,"about_ca_system_score_gemma":0.0001247751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000301145,"about_ca_topic_score_gemma":0.0005449366,"domain_scores_codex":[0.9999225,0.00001108887,0.000004651399,0.00001757881,0.00002629142,0.0000178061],"domain_scores_gemma":[0.999938,0.00001067976,0.0000197793,0.000004157798,0.00001399982,0.00001345116],"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":[0.000004425247,0.000004526307,0.00001792682,0.00001112811,0.00000108198,0.000006644414,0.000003758066,0.00003531234,0.9995784,0.00002130733,0.000006564737,0.0003089389],"study_design_scores_gemma":[0.000001519503,0.00004084734,0.0003145309,0.000001282545,0.000002969601,0.00002015857,0.000003765879,0.0006408251,0.9986013,0.000007528263,0.0003635453,0.000001685424],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897186,0.001015157,0.008311762,0.00004261437,0.00002418775,0.00002008128,0.00004169559,0.00007584166,0.0007501961],"genre_scores_gemma":[0.9925091,0.0006639538,0.005664629,0.00004109304,0.000007465841,0.00002037326,0.00006033193,0.00001655608,0.001016627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0003824395,"threshold_uncertainty_score":0.001262784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04623370304976176,"score_gpt":0.2989050628688725,"score_spread":0.2526713598191108,"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."}}