{"id":"W2740710461","doi":"10.1126/science.aah6362","title":"Tough adhesives for diverse wet surfaces","year":2017,"lang":"en","type":"article","venue":"Science","topic":"Surgical Sutures and Adhesives","field":"Medicine","cited_by":1553,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Science Foundation Ireland; National Institute of Dental and Craniofacial Research; European Commission; National Institutes of Health; National Science Foundation","keywords":"Adhesive; Biocompatibility; Materials science; Matrix (chemical analysis); Biomedical engineering; Composite material; Medicine; Metallurgy","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003330313,0.0009087606,0.0003991242,0.00102088,0.0005395617,0.0007163785,0.000413194,0.0007743982,0.003693367],"category_scores_gemma":[0.0004858333,0.0004069857,0.0003210249,0.00037822,0.0003956476,0.0006548064,0.0009241183,0.001139809,0.001358199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003600837,"about_ca_system_score_gemma":0.0002439052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001604208,"about_ca_topic_score_gemma":0.000569172,"domain_scores_codex":[0.9996915,0.00003753657,0.00002295436,0.0000566034,0.0001438689,0.00004759067],"domain_scores_gemma":[0.9996818,0.00005417559,0.00009565985,0.00002441708,0.00006810349,0.00007579906],"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":[0.0000288113,0.00003702759,0.0001676672,0.0003979224,0.0000207921,0.000308459,0.0001093623,0.0003653991,0.9700542,0.005296692,0.001437454,0.02177617],"study_design_scores_gemma":[0.00004169438,0.0008454166,0.003238816,0.0001857143,0.00007362411,0.00229055,0.000222701,0.003249231,0.8400477,0.003937049,0.1457854,0.00008217069],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.676189,0.09307075,0.1787264,0.002567249,0.003195559,0.0007393752,0.001132031,0.001759951,0.04261968],"genre_scores_gemma":[0.8444972,0.01966465,0.09702788,0.001952771,0.0004723127,0.0008471258,0.000735183,0.0002379077,0.03456492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003693367,"threshold_uncertainty_score":0.01235557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05431544303346474,"score_gpt":0.3592102266603362,"score_spread":0.3048947836268714,"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."}}