{"id":"W4388348234","doi":"10.1016/j.mtla.2023.101937","title":"An experimental and computational framework to investigate the thermal cycling approach for strengthening low SFE FeMnNi medium entropy alloy","year":2023,"lang":"en","type":"article","venue":"Materialia","topic":"High Entropy Alloys Studies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Indian Space Research Organisation; Department of Science and Technology, Ministry of Science and Technology, India; Science and Engineering Research Board; TDC Research; Tata Consultancy Services","keywords":"Materials science; Temperature cycling; Microstructure; Isothermal process; Grain size; Electron backscatter diffraction; Grain growth; Recrystallization (geology); Metallurgy; Ternary operation; Alloy; Composite material; Thermal; Thermodynamics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006094144,0.0004796834,0.000630369,0.0005627453,0.0007576896,0.0008920119,0.002096107,0.001459624,0.005532735],"category_scores_gemma":[0.001242805,0.0003896198,0.0005187535,0.0003625906,0.001436112,0.001001933,0.0006934316,0.001150301,0.000307086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008736515,"about_ca_system_score_gemma":0.0009775595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004157201,"about_ca_topic_score_gemma":0.00436272,"domain_scores_codex":[0.9998516,0.00003376805,0.000006185468,0.00002099619,0.00006202094,0.00002542408],"domain_scores_gemma":[0.9997515,0.0001053201,0.00002708957,0.00005181609,0.00005201293,0.00001236983],"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":[0.0001259734,0.0003588067,0.0008571396,0.0002615874,0.00002718272,0.0001470726,0.000101574,0.7067412,0.03391464,0.2492476,0.0006556264,0.007561627],"study_design_scores_gemma":[0.00001294409,0.00003209178,0.000265136,0.000008143398,0.000003977073,0.00001276297,0.00001988184,0.9886879,0.002034932,0.008239308,0.0006753952,0.000007453379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5362962,0.001378475,0.2918519,0.002019852,0.0002925301,0.0005395613,0.00138071,0.0004341501,0.1658065],"genre_scores_gemma":[0.9562865,0.0002810441,0.03630828,0.0001236951,0.000048035,0.0003109418,0.0002023991,0.0000743777,0.006364739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005532735,"threshold_uncertainty_score":0.01850885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01795628560376055,"score_gpt":0.2609032517672054,"score_spread":0.2429469661634449,"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."}}