{"id":"W101995230","doi":"10.1007/978-94-007-1013-9_6","title":"Probabilistic Multi-Scale Modeling of Materials","year":2004,"lang":"en","type":"book-chapter","venue":"ICASE/LaRC interdisciplinary series in science and engineering","topic":"Composite Material Mechanics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Homogenization (climate); Probabilistic logic; Macroscopic scale; Anisotropy; Multiscale modeling; Stiffness; Statistical physics; Materials science; Constitutive equation; Material properties; Scale (ratio); Computer science; Finite element method; Engineering; Structural engineering; Composite material; Artificial intelligence; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004536176,0.0004711421,0.0006398291,0.0005839709,0.0000885116,0.0001109848,0.0004990312,0.0002253529,0.00005622704],"category_scores_gemma":[0.00003290111,0.0005021775,0.00005924605,0.0001699065,0.0002731865,0.0005124654,0.001015889,0.0003128965,0.000007673891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003978684,"about_ca_system_score_gemma":0.0000808853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007157497,"about_ca_topic_score_gemma":0.00002169112,"domain_scores_codex":[0.9979436,0.000004481672,0.0007100384,0.0004912987,0.0003824621,0.0004681041],"domain_scores_gemma":[0.9991956,0.00002331232,0.00006996239,0.0004621603,0.0001174151,0.000131601],"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":[0.00005342814,0.00002287165,9.384704e-7,0.00202991,0.00002755361,0.0001113727,0.00201866,0.6784456,0.2989788,0.01779107,0.0000043517,0.0005154781],"study_design_scores_gemma":[0.0006332788,0.0003242525,0.00002594292,0.004985288,0.00006516169,0.0003150217,0.0002345803,0.9222891,0.05389279,0.01541356,0.0002721599,0.001548831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9484503,0.001296763,0.01387244,0.0001169582,0.006408529,0.00185282,0.0004064322,0.001035508,0.02656021],"genre_scores_gemma":[0.9933121,0.0001251876,0.005730574,0.000003441799,0.0001059961,0.0000317302,0.00001352278,0.0001180056,0.0005594064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.245086,"threshold_uncertainty_score":0.999743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150428084165636,"score_gpt":0.2348523476824531,"score_spread":0.2198095392658895,"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."}}