{"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 minerals, 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001772014,0.0004673183,0.0003636953,0.0004520023,0.0003050518,0.0004585633,0.0006905862,0.00103876,0.001408981],"category_scores_gemma":[0.0005406868,0.0005325266,0.0005903296,0.0003038794,0.0004166514,0.0007175249,0.0002067625,0.0005275405,0.000443807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006256036,"about_ca_system_score_gemma":0.0005274329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006853507,"about_ca_topic_score_gemma":0.00653345,"domain_scores_codex":[0.999964,0.000006929133,0.000001853479,0.000007470568,0.00001293234,0.000006857942],"domain_scores_gemma":[0.9998454,0.00009524426,0.00001567065,0.0000134523,0.0000225338,0.000007797439],"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":[0.0000220376,0.00003012763,0.0005924099,0.00001826371,0.000004458394,0.00003498047,0.0000157063,0.9887011,0.004163861,0.001695292,0.000279983,0.004441822],"study_design_scores_gemma":[0.00000217348,0.000005465738,0.0002543684,0.000001382548,0.000001138876,0.00000616165,0.000002752078,0.9986122,0.0004060711,0.0006121763,0.00009367591,0.000002468848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6179273,0.001123179,0.3498806,0.000378214,0.0001124315,0.0001182525,0.0006315694,0.001024142,0.02880451],"genre_scores_gemma":[0.9790493,0.0004706792,0.01466356,0.00002578001,0.00002330881,0.00005355044,0.0002567043,0.00006991906,0.005387025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006853507,"threshold_uncertainty_score":0.01362723,"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."}}