{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001143382,0.0002346583,0.0002318778,0.0001670513,0.0002461967,0.0004134731,0.0004646967,0.0005578745,0.001265487],"category_scores_gemma":[0.0006336457,0.0002990644,0.0003234337,0.0002201588,0.0002574923,0.0002973951,0.0001107225,0.0002209265,0.0001674561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007168599,"about_ca_system_score_gemma":0.0005509804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01467019,"about_ca_topic_score_gemma":0.01227653,"domain_scores_codex":[0.9999539,0.000007830659,0.000001474914,0.000008600584,0.0000160029,0.0000123238],"domain_scores_gemma":[0.9998605,0.00006976769,0.00001943243,0.00001495532,0.00002652797,0.00000867296],"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":[0.00007648313,0.0000254542,0.00167758,0.00002977778,0.00000917294,0.00007386367,0.00003521737,0.9802552,0.0126099,0.001494319,0.0001898352,0.003523094],"study_design_scores_gemma":[0.000004113524,0.000008364479,0.0005204931,7.727963e-7,0.000002168601,0.000009136442,0.000004739716,0.9977152,0.001413692,0.0001436218,0.000175765,0.000001899661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8937967,0.0004476084,0.08707644,0.0002373511,0.00004977023,0.00006667474,0.0004033639,0.0004477976,0.01747422],"genre_scores_gemma":[0.9950405,0.00007997745,0.003513009,0.000007406979,0.000004493943,0.00001049129,0.0000693497,0.00004534318,0.001229419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01467019,"threshold_uncertainty_score":0.02916962,"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."}}