{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008804204,0.0001100161,0.0002165487,0.00009979643,0.00002461772,0.00007339518,0.0002450576,0.00006133963,0.0005249311],"category_scores_gemma":[0.00006831662,0.00009920308,0.00002931755,0.00004808826,0.00003887401,0.000397013,0.0001090557,0.00005537161,0.00002115225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001413857,"about_ca_system_score_gemma":0.00001235973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006334779,"about_ca_topic_score_gemma":0.00001133103,"domain_scores_codex":[0.9986826,0.00009803267,0.0006485286,0.0001121329,0.000348465,0.0001102644],"domain_scores_gemma":[0.9991992,0.0000682646,0.0005504834,0.00007472139,0.00006508957,0.0000422207],"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":[0.0002481775,0.00002654809,0.00107257,0.000007190965,0.00003300549,0.000003546954,0.000295306,0.01210272,0.9846074,0.0005925454,0.0001305353,0.000880449],"study_design_scores_gemma":[0.0003976664,0.000110052,0.000628343,0.0001944925,0.00001243266,0.0001048469,0.0001101536,0.0003371933,0.9958338,0.001153555,0.001022247,0.00009524214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747998,0.00005819948,0.02247679,0.000126312,0.002400128,0.0001044175,0.00001110255,0.000006270785,0.00001704569],"genre_scores_gemma":[0.9797478,0.0005095907,0.0195216,0.00001706949,0.000163777,0.000002838809,0.000003049291,0.000009094813,0.00002514226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01176553,"threshold_uncertainty_score":0.5747627,"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."}}