{"id":"W2768782532","doi":"10.1021/acs.biomac.7b01243","title":"Autonomously Self-Adhesive Hydrogels as Building Blocks for Additive Manufacturing","year":2017,"lang":"en","type":"article","venue":"Biomacromolecules","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China; Canada Foundation for Innovation; Ontario Innovation Trust","keywords":"Self-healing hydrogels; Adhesive; Vinyl alcohol; Hyaluronic acid; Materials science; Chemical engineering; Cell encapsulation; Self-healing; Chemistry; Nanotechnology; Composite material; Polymer chemistry; Polymer; Layer (electronics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003051044,0.0002743761,0.0002569929,0.0001510662,0.0005646491,0.0003478899,0.0009080694,0.0001940454,0.0001033594],"category_scores_gemma":[0.0004274091,0.0002788972,0.0001589688,0.00004641621,0.0001753353,0.0001604883,0.0002797089,0.000200291,0.0001465097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630398,"about_ca_system_score_gemma":0.00006351501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005596617,"about_ca_topic_score_gemma":0.000007093577,"domain_scores_codex":[0.9982516,0.00002211482,0.000265081,0.000403011,0.0003322672,0.0007258932],"domain_scores_gemma":[0.9986798,0.0002452481,0.00009018236,0.0006559747,0.0000681392,0.0002606719],"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":[0.00006117013,0.0001457741,0.0001809246,0.0005392804,0.000965873,0.0005345602,0.0007340502,0.0001196939,0.7043347,0.003948091,0.007758447,0.2806774],"study_design_scores_gemma":[0.0004945207,0.00007181986,0.0008611051,0.00009080811,0.00002535737,0.00003314144,0.00002411696,0.004446152,0.9406101,0.002091727,0.05090041,0.000350754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747737,0.0001981181,0.01138034,0.0002826744,0.0005332629,0.0006561529,0.0001098643,0.0009087066,0.01115715],"genre_scores_gemma":[0.9802985,0.00005024061,0.01887632,0.000036001,0.0002808077,0.0001503005,0.00001634778,0.00008475221,0.0002067399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2803266,"threshold_uncertainty_score":0.9999663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376792100837968,"score_gpt":0.2893805496218081,"score_spread":0.2756126286134284,"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."}}