{"id":"W4285230693","doi":"10.1007/978-3-031-06212-4_46","title":"Predicting the Flow and Failure Properties of Dual-Phase Steel Using Phenomenological Models","year":2022,"lang":"en","type":"book-chapter","venue":"˜The œminerals, metals & materials series","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Martensite; Materials science; Microstructure; Ductility (Earth science); Phenomenological model; Flow (mathematics); Ultimate tensile strength; Ferrite (magnet); Flow stress; Deformation (meteorology); Austenite; Structural engineering; Metallurgy; Mechanics; Composite material; Engineering; Mathematics; Creep; 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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009865165,0.0007209647,0.00123588,0.000122983,0.000478837,0.0002495893,0.0004163541,0.0003013129,0.001792551],"category_scores_gemma":[0.00002327407,0.0004273039,0.0001574438,0.00005337501,0.0004407337,0.0006061292,0.0005453224,0.0003384162,0.000007975397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005204311,"about_ca_system_score_gemma":0.00004559563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000766918,"about_ca_topic_score_gemma":0.0000348402,"domain_scores_codex":[0.9974304,0.0001703411,0.001095024,0.0004163561,0.0004220417,0.0004658368],"domain_scores_gemma":[0.9987586,0.0000614313,0.0004016576,0.000631016,0.00007709273,0.00007017857],"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":[0.0001925294,0.00001760533,9.501812e-8,0.0008554026,0.0005613439,0.00001970647,0.001409427,0.01365041,0.9472947,0.03573929,0.00009810622,0.0001614625],"study_design_scores_gemma":[0.003514591,0.001331306,0.000003143587,0.0020681,0.004534454,0.001823299,0.003971195,0.03787247,0.4607942,0.06598781,0.4130069,0.005092533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713755,0.007705566,0.0001512734,0.0001406327,0.002856726,0.001553586,0.0009213805,0.0004489807,0.01484632],"genre_scores_gemma":[0.959121,0.001434027,0.00106423,0.00006532075,0.0008936207,0.0002032899,0.0001493063,0.0002804824,0.03678865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4865004,"threshold_uncertainty_score":0.9998178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04627900667545653,"score_gpt":0.218838980766741,"score_spread":0.1725599740912845,"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."}}