{"id":"W2108296987","doi":"10.1139/tcsme-2008-0013","title":"MICROSTRUCTURAL MODELING OF COLD CREEP/FATIGUE IN NEAR ALPHA TITANIUM ALLOYS USING CELLULAR AUTOMATA METHOD","year":2008,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Microstructure and mechanical properties","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Creep; Materials science; Titanium alloy; Cellular automaton; Microstructure; Titanium; Texture (cosmology); Alloy; Mechanics; Composite material; Structural engineering; Metallurgy; Computer science; Algorithm; Physics; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001527409,0.0004270666,0.0003950354,0.0005012371,0.0003825816,0.0005483481,0.0007490862,0.0008158894,0.0009302084],"category_scores_gemma":[0.000469597,0.0003108508,0.0006153159,0.000326631,0.000511141,0.0003816549,0.0002777409,0.000261026,0.0001602415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007889261,"about_ca_system_score_gemma":0.000674571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02178817,"about_ca_topic_score_gemma":0.01510492,"domain_scores_codex":[0.9999236,0.00001282769,0.000005855443,0.00002285836,0.00002311438,0.00001171966],"domain_scores_gemma":[0.9997978,0.0000909454,0.00003137422,0.00002193605,0.00004520473,0.00001281177],"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":[0.000008006854,0.000007369635,0.0004074249,0.000009492536,0.000005592302,0.00003284692,0.00002482065,0.9921022,0.004201686,0.001030325,0.00003482858,0.002135421],"study_design_scores_gemma":[7.714648e-7,0.000003925201,0.0001105008,8.853809e-7,0.000001677895,0.000004582495,0.000002972988,0.9992387,0.0003306007,0.0002051732,0.00009852331,0.000001652988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4114732,0.0005765622,0.5782573,0.0001630571,0.00005926333,0.00008433104,0.0002427047,0.0006688581,0.008474746],"genre_scores_gemma":[0.9639629,0.0002063804,0.03342637,0.00001621319,0.000008544735,0.00008457322,0.00008368953,0.00002622831,0.00218519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02178817,"threshold_uncertainty_score":0.04332274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03448578344252367,"score_gpt":0.2400056615482281,"score_spread":0.2055198781057044,"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."}}