{"id":"W1982861290","doi":"10.1007/s10999-005-0001-5","title":"Intelligent Condition Monitoring of Aerospace Composites: Part I - Nano Reinforced Surfaces &amp; Interfaces","year":2005,"lang":"en","type":"article","venue":"International Journal of Mechanics and Materials in Design","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Materials science; Composite material; Solid mechanics; Aerospace; Multiscale modeling; Finite element method; Adhesive; Composite number; Toughness; Handshaking; Fracture toughness; Fracture mechanics; Structural engineering; Layer (electronics); Computer science; 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":[0.0001638719,0.0002278877,0.0002755231,0.0004312657,0.0002269761,0.0004994284,0.0003335165,0.0003925333,0.002110102],"category_scores_gemma":[0.0004972066,0.0001631343,0.0001256486,0.0002038909,0.0002270581,0.0006043709,0.000226192,0.000215515,0.0002949616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002396704,"about_ca_system_score_gemma":0.0001246503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005798742,"about_ca_topic_score_gemma":0.0009839311,"domain_scores_codex":[0.9998345,0.00002051227,0.00000840151,0.00003060049,0.0000934546,0.00001252621],"domain_scores_gemma":[0.9997566,0.0000862204,0.00004037798,0.0000333415,0.00007212212,0.00001132926],"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":[0.0003650619,0.0001058362,0.005211706,0.00009914131,0.00002047294,0.00005564703,0.00009377805,0.006770949,0.8277417,0.0008381262,0.001193135,0.1575045],"study_design_scores_gemma":[0.00001408809,0.000183162,0.02418842,0.00001310082,0.00003377089,0.0002659982,0.0000780465,0.156209,0.8137905,0.001025726,0.004175754,0.00002248661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8152205,0.002444112,0.1715273,0.0003525413,0.0001372819,0.0000548564,0.000194308,0.001000651,0.009068316],"genre_scores_gemma":[0.972814,0.0004545041,0.02308998,0.0000434778,0.00003328934,0.00001897934,0.00008352854,0.0000517446,0.003410481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002110102,"threshold_uncertainty_score":0.007058978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02484443341125415,"score_gpt":0.2666637791461466,"score_spread":0.2418193457348925,"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."}}