{"id":"W942235198","doi":"","title":"Modeling of texture and microstructure transformation of metals during annealing","year":2005,"lang":"en","type":"article","venue":"Archives of Metallurgy and Materials","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Materials science; Microstructure; Industrial chemistry; Materials processing; Annealing (glass); Metallurgy; Transformation (genetics); Texture (cosmology); Process engineering; Artificial intelligence; Computer science; Engineering; Biochemical engineering","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":[0.0001814214,0.0001474855,0.0004656059,0.0001303549,0.00003533017,0.00001070343,0.00007336355,0.00006839464,0.00005566014],"category_scores_gemma":[0.00001347825,0.0001285065,0.00005186505,0.00003759802,0.0000822945,0.0002501338,0.00003234414,0.00004289389,3.045457e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000177309,"about_ca_system_score_gemma":0.000004998734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002031396,"about_ca_topic_score_gemma":0.00000507197,"domain_scores_codex":[0.9990875,0.00003495756,0.0005536401,0.0001058107,0.00007784662,0.0001402863],"domain_scores_gemma":[0.9997102,0.00003126477,0.00009537353,0.0001080302,0.00001503635,0.00004008707],"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":[0.00008092491,0.000005698794,0.00000181582,0.001293179,0.0001022001,2.882715e-7,0.001862537,0.02633044,0.9657503,0.002367395,1.313963e-7,0.002205084],"study_design_scores_gemma":[0.0004297247,0.00002373466,0.0003146124,0.000148005,0.00007847453,0.00001982427,0.0001217427,0.0188119,0.9786749,0.001192377,0.00006162989,0.0001230333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922129,0.0009043537,0.006223274,0.00001141781,0.000107256,0.000123057,0.00004860102,0.00002482465,0.0003443483],"genre_scores_gemma":[0.993559,0.001014285,0.005335839,0.000003117943,0.00003876985,0.000003445735,0.00001612849,0.00001638659,0.00001302666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01292463,"threshold_uncertainty_score":0.5240343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005865413476347849,"score_gpt":0.193144536043743,"score_spread":0.1872791225673952,"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."}}