{"id":"W4224282785","doi":"10.1007/s11837-022-05303-2","title":"Revealing Non-Uniform Work-Hardening in a Heterogeneous Structured Steel Using High-Speed Nanoindentation","year":2022,"lang":"en","type":"article","venue":"JOM","topic":"Metal and Thin Film Mechanics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Nanoindentation; Materials science; Microstructure; Austenite; Work hardening; Metallurgy; Composite material; Deformation (meteorology); Elastic modulus; Hardening (computing); Lamellar structure; Ductility (Earth science); Diffusionless transformation; Tensile testing; Ultimate tensile strength; Creep","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001858294,0.0001301575,0.0001757159,0.0001440255,0.0001173663,0.00003586346,0.0001334524,0.0000492741,0.0001897081],"category_scores_gemma":[0.000008342229,0.0001460422,0.00005109882,0.000349508,0.000004011401,0.00008665102,0.00008687881,0.0002716263,0.000006137969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002242698,"about_ca_system_score_gemma":0.00001397014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004910492,"about_ca_topic_score_gemma":0.00002779285,"domain_scores_codex":[0.9990721,0.00003055763,0.0002594882,0.0001573721,0.0002453287,0.0002351001],"domain_scores_gemma":[0.9997317,0.00001039822,0.00004951455,0.0001525001,0.00001217057,0.00004378126],"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":[0.00002316538,0.00001028951,0.0001959998,0.00004834891,0.00002922065,0.00007046667,0.0008074085,0.8748932,0.1219317,0.0001232676,0.00007770567,0.001789246],"study_design_scores_gemma":[0.00111466,0.000055916,0.0009482678,0.0000790108,0.00003750762,0.00009313523,0.0004761014,0.9441336,0.05014802,0.001172424,0.001234624,0.0005067812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948409,0.0001258644,0.002564946,0.000005833499,0.001999794,0.0001685252,0.00001656839,0.0001016737,0.0001759561],"genre_scores_gemma":[0.9942517,0.000004497536,0.005315531,0.00003986564,0.00008087928,0.000009918336,0.0000348224,0.0000396359,0.0002231302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07178367,"threshold_uncertainty_score":0.5955427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617262301712109,"score_gpt":0.2167988885684908,"score_spread":0.2006262655513697,"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."}}