{"id":"W4406999417","doi":"10.1016/j.ijplas.2025.104255","title":"Thermodynamically consistent damage evolution model coupled with rate-dependent crystal plasticity: Application to high-strength low alloy steel at various strain rates","year":2025,"lang":"en","type":"article","venue":"International Journal of Plasticity","topic":"High-Velocity Impact and Material Behavior","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Motors (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada","keywords":"Materials science; Plasticity; Crystal plasticity; Strain rate; Alloy; Strain (injury); Metallurgy; Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003239605,0.0005289665,0.0004369808,0.0004267209,0.0002473211,0.0004243408,0.001168952,0.0009058448,0.0007996663],"category_scores_gemma":[0.0004715868,0.0003433349,0.0006884391,0.0002760884,0.0003838214,0.0005100337,0.0003042833,0.0004243614,0.0001743836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006891806,"about_ca_system_score_gemma":0.0008021937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008191478,"about_ca_topic_score_gemma":0.006624189,"domain_scores_codex":[0.9998492,0.00002087077,0.00000917621,0.00003524471,0.00006977083,0.00001576178],"domain_scores_gemma":[0.9997699,0.00005605349,0.00006076141,0.00002583099,0.00007209701,0.00001532577],"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":[0.00002346254,0.00004875481,0.001450318,0.00005858634,0.00001541037,0.000168584,0.00004708685,0.9654523,0.02369983,0.002874343,0.0001472394,0.006014034],"study_design_scores_gemma":[8.97863e-7,0.000008654983,0.000216755,8.97821e-7,0.000001639817,0.00001215717,0.00000188173,0.9988829,0.0006833895,0.0001376447,0.00005069483,0.000002476948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4681222,0.001036786,0.519549,0.0002767489,0.00005738446,0.0001771524,0.0002884044,0.0005178564,0.009974434],"genre_scores_gemma":[0.9831806,0.000279548,0.01335251,0.00002040725,0.00000811986,0.00007076114,0.0001023934,0.00003470831,0.00295099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008191478,"threshold_uncertainty_score":0.01628757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007259795433981045,"score_gpt":0.2564764231220193,"score_spread":0.2492166276880383,"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."}}