{"id":"W4388807485","doi":"10.4325/seikeikakou.35.404","title":"Prediction of Interfacial Adhesion Strength in CFRTP considering Plasticity of Matrix Resin using Numerical Material Testing and Neural Network","year":2023,"lang":"en","type":"article","venue":"Seikei-Kakou","topic":"Mechanical Behavior of Composites","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cybernet Systems Corporation (Canada)","funders":"","keywords":"Materials science; Composite material; Ultimate tensile strength; Fracture (geology); Volume fraction; Adhesion; Fiber; Matrix (chemical analysis); Thermoplastic","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":[0.0003621054,0.0007619885,0.0003741946,0.0005501772,0.0001978049,0.0003011287,0.0005534983,0.0007292253,0.0005034848],"category_scores_gemma":[0.001171872,0.0003303356,0.0003945446,0.0002607442,0.0003557617,0.0004869558,0.0003078487,0.0003599492,0.00009705773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004386425,"about_ca_system_score_gemma":0.0004991053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006261521,"about_ca_topic_score_gemma":0.006529997,"domain_scores_codex":[0.999818,0.00002645083,0.00001114275,0.00005589269,0.00006841301,0.00002016038],"domain_scores_gemma":[0.9995939,0.0001763879,0.0001043628,0.00002456834,0.00008145432,0.00001921331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002945563,0.0000402451,0.003166081,0.00002907032,0.00001694554,0.00006621912,0.00001201553,0.9596652,0.01195818,0.0002983317,0.00005625472,0.02466211],"study_design_scores_gemma":[5.305054e-7,0.000005794249,0.0003243139,7.025736e-7,0.000001178074,0.000003832932,9.148173e-7,0.9987044,0.000889655,0.00005525076,0.00001211612,0.000001234279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3549054,0.000232475,0.6424259,0.000101238,0.00002264199,0.00003334603,0.00006390295,0.0004842904,0.001730879],"genre_scores_gemma":[0.9612507,0.00007870543,0.03795669,0.00001670508,0.000006777521,0.00004328731,0.00004198712,0.00001526703,0.0005899621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006261521,"threshold_uncertainty_score":0.01245016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04754420832417564,"score_gpt":0.263080398395361,"score_spread":0.2155361900711853,"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."}}