{"id":"W4390459489","doi":"10.3390/met14010049","title":"Predictive Modeling of Hardness Values and Phase Fraction Percentages in Micro-Alloyed Steel during Heat Treatment Using AI","year":2023,"lang":"en","type":"article","venue":"Metals","topic":"Microstructure and Mechanical Properties of Steels","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Continuous cooling transformation; Materials science; Experimental data; Artificial neural network; Work (physics); Fraction (chemistry); Metallurgy; Phase (matter); Biological system; Computer science; Microstructure; Mechanical engineering; Machine learning; Engineering; Statistics; Mathematics; Austenite; Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.00009436437,0.0001431384,0.0002855611,0.0001362585,0.00004493608,0.00001926832,0.00004431669,0.00006574407,0.000014883],"category_scores_gemma":[0.000009874441,0.0001213356,0.00005194519,0.0001086247,0.0000160339,0.0002180852,0.00002988791,0.00007832731,0.000002692917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001164809,"about_ca_system_score_gemma":0.000007462633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009264022,"about_ca_topic_score_gemma":0.000006672188,"domain_scores_codex":[0.9992841,0.0000250357,0.0002467192,0.0001677868,0.00008766029,0.000188727],"domain_scores_gemma":[0.9997948,0.00001323463,0.00001635077,0.0001111497,0.00002120281,0.00004326249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004243775,0.00002144909,0.00005390793,0.00009413216,0.00005876262,0.000003831409,0.0007118371,0.2035121,0.7949354,0.000001081641,0.000005473908,0.0005595759],"study_design_scores_gemma":[0.0008982777,0.00005296529,0.0001692511,0.00007395174,0.00004680323,0.000004411921,0.0004436723,0.5380883,0.4600437,0.00005945344,0.00002581791,0.00009341963],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962357,0.001056911,0.0021443,0.00001647682,0.0001503331,0.0002306578,0.0000296698,0.0001051811,0.00003073396],"genre_scores_gemma":[0.9989426,0.0004730381,0.0004150835,0.000008808059,0.00004192726,0.00001086877,0.000009631585,0.00002621996,0.0000718628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3348917,"threshold_uncertainty_score":0.4947921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0301441473716458,"score_gpt":0.2815402717326784,"score_spread":0.2513961243610326,"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."}}