{"id":"W4410162886","doi":"10.1016/j.pnsc.2025.04.002","title":"Accurate reconstruction and prediction of T55511 titanium alloy microstructure using DDPM model and quantitative evaluation","year":2025,"lang":"en","type":"article","venue":"Progress in Natural Science Materials International","topic":"Titanium Alloys Microstructure and Properties","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Key Research and Development Projects of Shaanxi Province; University of Waterloo; Natural Science Foundation of Inner Mongolia; Inner Mongolia University; National Natural Science Foundation of China","keywords":"Materials science; Microstructure; Titanium alloy; Alloy; Metallurgy; Titanium","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":[0.001132395,0.0001468394,0.000194324,0.0003006027,0.0001553443,0.0002873307,0.0002678867,0.0000837913,0.00005774643],"category_scores_gemma":[0.0001505629,0.0001182137,0.00001562675,0.0002594693,0.001022198,0.00102282,0.0002082359,0.00009022069,6.945653e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001576658,"about_ca_system_score_gemma":0.0002259617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005846061,"about_ca_topic_score_gemma":0.00002765252,"domain_scores_codex":[0.9984745,0.00008037664,0.0004149524,0.0004212725,0.0004058236,0.0002030675],"domain_scores_gemma":[0.999146,0.00003031571,0.000223445,0.0001124642,0.0004585753,0.00002919636],"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":[0.0001610568,0.000008266112,0.003585393,0.00003925745,0.000007245836,3.836806e-7,0.0002656402,0.0003465262,0.989432,0.003273744,0.000004459254,0.002876028],"study_design_scores_gemma":[0.000576525,0.00003810586,0.0364267,0.000230085,0.00002582857,0.00005164582,0.0002480905,0.09814905,0.8594338,0.004664157,0.00001248612,0.0001434739],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951458,0.001213057,0.0001151586,0.0001390715,0.002816048,0.0003252896,0.0001306719,0.00002174177,0.00009317126],"genre_scores_gemma":[0.9823144,0.00005008959,0.0175181,0.00003069663,0.00003743083,0.00001587275,0.00001175811,0.000005595424,0.00001605617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1299981,"threshold_uncertainty_score":0.4820616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02880055306730181,"score_gpt":0.328038319177599,"score_spread":0.2992377661102972,"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."}}