{"id":"W2574816874","doi":"10.1590/1980-5373-mr-2016-0795","title":"Softening Mechanisms of the AISI 410 Martensitic Stainless Steel Under Hot Torsion Simulation","year":2017,"lang":"en","type":"article","venue":"Materials Research","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão; Conselho Nacional de Desenvolvimento Científico e Tecnológico; McGill University","keywords":"Materials science; Softening; Strain rate; Isothermal process; Metallurgy; Martensite; Microstructure; Dynamic recrystallization; Toughness; Torsion (gastropod); Austenite; Carbide; Composite material; Thermodynamics; Hot working","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001481923,0.0001082602,0.0002040498,0.00007931684,0.0005132263,0.0002468601,0.0004162288,0.00010605,0.0005143401],"category_scores_gemma":[0.0001951303,0.00007952692,0.00003743726,0.0000541222,0.00009406129,0.0002272464,0.0002846771,0.0001119148,0.00005318144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000056369,"about_ca_system_score_gemma":0.0000244128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009153756,"about_ca_topic_score_gemma":0.00001072137,"domain_scores_codex":[0.9986728,0.0001527865,0.0002495623,0.0001423133,0.0004346058,0.0003479031],"domain_scores_gemma":[0.9990584,0.00008150536,0.00006235384,0.0005991502,0.0001507138,0.00004782361],"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":[0.00004646765,0.000006906367,0.000006124092,0.0001787354,0.000015938,0.000002781401,0.00007728893,0.00974954,0.9827364,0.006837115,0.00003340534,0.0003092711],"study_design_scores_gemma":[0.0002177847,0.00002722812,0.003139901,0.0001198077,0.000008000972,0.000001877991,0.00008610449,0.008252362,0.9810609,0.006709058,0.0002735761,0.0001033927],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945942,0.00001152119,0.002917098,0.00005133738,0.001371261,0.0002664569,0.00001380163,0.00004899089,0.0007253366],"genre_scores_gemma":[0.9991016,0.000007856597,0.0002515618,0.000008357627,0.0001051632,0.0000139136,0.000005066522,0.00003180742,0.0004746643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004507413,"threshold_uncertainty_score":0.5631663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08797316950715264,"score_gpt":0.3486219767414299,"score_spread":0.2606488072342772,"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."}}