{"id":"W4406642400","doi":"10.1016/j.jmrt.2025.01.120","title":"Investigating the high-strain rate response of additively manufactured 420 stainless steel through material constitutive modelling","year":2025,"lang":"en","type":"article","venue":"Journal of Materials Research and Technology","topic":"High-Velocity Impact and Material Behavior","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Department of Science and Technology, Ministry of Science and Technology, India; New Brunswick Innovation Foundation; Canada Foundation for Innovation; University of New Brunswick","keywords":"Materials science; Constitutive equation; Strain rate; Strain (injury); Metallurgy; Composite material; Finite element method; Structural engineering","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":[0.006374869,0.0002386199,0.0007303973,0.0005934736,0.0004299726,0.0002942089,0.000743017,0.000335407,0.0003241448],"category_scores_gemma":[0.001374815,0.0001579702,0.00004460943,0.000494295,0.002593196,0.0003678173,0.0004321675,0.0004085308,0.000009316267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001056772,"about_ca_system_score_gemma":0.000748548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002058692,"about_ca_topic_score_gemma":0.00001021155,"domain_scores_codex":[0.9962875,0.001293046,0.00101918,0.0002785912,0.0004845617,0.0006371372],"domain_scores_gemma":[0.9972733,0.0007263595,0.0006085664,0.0003319746,0.0009610022,0.00009879011],"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":[0.002079849,0.00006352425,0.0000218437,0.0001035624,0.00004172713,0.00005424139,0.0004143117,0.00003693216,0.9760221,0.02063226,0.0004001285,0.000129526],"study_design_scores_gemma":[0.0009500339,0.0006424937,0.0005773202,0.0003249226,0.00003279345,0.00006657163,0.001310507,0.000003347886,0.9634537,0.03184442,0.0006658261,0.0001280837],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935886,0.00008679143,0.0006853215,0.003115791,0.0009364226,0.0004484502,0.001062723,0.0000385083,0.00003734139],"genre_scores_gemma":[0.9965574,0.0001205892,0.002988499,0.00004882224,0.00012359,0.00002573996,0.000008939995,0.0000169635,0.0001094341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01256841,"threshold_uncertainty_score":0.9554739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05965052958671042,"score_gpt":0.355703620534528,"score_spread":0.2960530909478176,"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."}}