{"id":"W1957541178","doi":"10.1007/s11340-015-0042-0","title":"Identifying Hyper-Viscoelastic Model Parameters from an Inflation-Extension Test and Ultrasound Images","year":2015,"lang":"en","type":"article","venue":"Experimental Mechanics","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Nautical Research Society","funders":"Institut National des Sciences Appliquées de Lyon; Agence Nationale de la Recherche","keywords":"Viscoelasticity; Hyperelastic material; Materials science; Biomedical engineering; Mechanics; Viscosity; Ultrasound; Finite element method; Acoustics; Computer science; Structural engineering; Composite material; Physics; Engineering","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":[0.0005735461,0.0004085203,0.0003553802,0.001032695,0.0001520023,0.0005190602,0.0004245299,0.001315853,0.001416406],"category_scores_gemma":[0.003063146,0.0002349588,0.000341063,0.0004135534,0.00035695,0.0006686517,0.0004083412,0.0004150587,0.0004728506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001433133,"about_ca_system_score_gemma":0.0002470399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007468833,"about_ca_topic_score_gemma":0.0008387479,"domain_scores_codex":[0.9998078,0.0000419547,0.00002113243,0.00003569486,0.00006812376,0.00002537181],"domain_scores_gemma":[0.9988272,0.0006254134,0.0001706844,0.0001338919,0.0001892863,0.00005360641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001857159,0.000679212,0.04256546,0.0003732219,0.00009332916,0.00122385,0.000351475,0.05004423,0.7926306,0.0007159198,0.0006686382,0.1087968],"study_design_scores_gemma":[0.00006693234,0.001264896,0.1796326,0.00006350869,0.0001217818,0.001890841,0.000359531,0.6425996,0.1711957,0.001609527,0.001065504,0.0001296214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9009453,0.0002323753,0.09654033,0.0001056843,0.00003246853,0.00009900478,0.0004140693,0.0003678846,0.001262941],"genre_scores_gemma":[0.9884589,0.00009525871,0.01059476,0.00003244561,0.00001064442,0.0000363436,0.0002435034,0.00003168084,0.0004963876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001416406,"threshold_uncertainty_score":0.00473839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06064990959937224,"score_gpt":0.3397253362598759,"score_spread":0.2790754266605037,"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."}}