{"id":"W3024038249","doi":"10.1371/journal.pone.0233021","title":"Mechanical characterization of PVA hydrogels’ rate-dependent response using multi-axial loading","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Elasticity and Material Modeling","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Viscoelasticity; Materials science; Self-healing hydrogels; Indentation; Composite material; Ultimate tensile strength; Characterization (materials science); Natural rubber; Finite element method; Tensile testing; Material properties; Compression (physics); Structural engineering; Nanotechnology; Polymer chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001499672,0.0001028632,0.0002142497,0.0000413788,0.00003348681,0.00002085203,0.0000817336,0.00007986739,0.00004717592],"category_scores_gemma":[0.000147346,0.0001189928,0.00002777362,0.00008476987,0.000009371576,0.0001294028,0.000043403,0.0001029383,0.00002468008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003584114,"about_ca_system_score_gemma":0.00001371337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004541826,"about_ca_topic_score_gemma":8.022006e-7,"domain_scores_codex":[0.9992605,0.00005965535,0.0002527524,0.0001311992,0.0001433578,0.0001525194],"domain_scores_gemma":[0.9997197,0.0000392162,0.00004084698,0.00008245352,0.00003662971,0.00008113388],"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":[0.0002435659,0.00008812191,0.00001964775,0.0001682553,0.00005582847,0.000004462223,0.0002888602,0.007642341,0.9914364,0.00002954337,1.463804e-7,0.00002284402],"study_design_scores_gemma":[0.0002277008,0.00002959022,0.00003875459,0.00008885421,0.00003656431,3.99999e-7,0.000007445341,0.414323,0.5851687,0.000004913528,0.000001927296,0.00007213499],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8704588,0.000007237311,0.1290876,0.00004993152,0.00008638464,0.0001204368,0.00002095465,0.0001631915,0.000005346823],"genre_scores_gemma":[0.9943893,0.00001262551,0.005358691,0.00004494417,0.0001401821,0.000004175185,0.000009997683,0.00003520759,0.000004893208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4066806,"threshold_uncertainty_score":0.4852385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0811800191641896,"score_gpt":0.2239403041607451,"score_spread":0.1427602849965555,"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."}}