{"id":"W4407028637","doi":"10.1016/j.jmrt.2025.01.220","title":"Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling","year":2025,"lang":"en","type":"article","venue":"Journal of Materials Research and Technology","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Novelis (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Novelis","keywords":"Materials science; Recrystallization (geology); Aluminium; Thermal; Dynamic recrystallization; Metallurgy; Engineering physics; Thermodynamics; Hot working; Microstructure; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00027115,0.0005003471,0.0004886471,0.000477417,0.0003410944,0.0007282499,0.000995146,0.0008607329,0.001335547],"category_scores_gemma":[0.0003927336,0.0004626045,0.0006430957,0.0004003078,0.0003261412,0.0005394066,0.0003160016,0.000442824,0.0003100886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008011902,"about_ca_system_score_gemma":0.001376511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02147427,"about_ca_topic_score_gemma":0.0258542,"domain_scores_codex":[0.9999323,0.00001259419,0.000003651859,0.00001373132,0.00002920307,0.000008440352],"domain_scores_gemma":[0.9998956,0.00004016574,0.00001481521,0.00001316217,0.00003123583,0.000005121009],"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":[0.000006999202,0.00001509482,0.0001597344,0.00002151014,0.000005754521,0.00001321815,0.00001087463,0.9925666,0.002749791,0.0009811575,0.00007721332,0.003392061],"study_design_scores_gemma":[0.000001393021,0.000004003998,0.00004558561,0.000001623578,0.000001253544,0.000001988242,0.000001712322,0.9993364,0.0002597671,0.0001595542,0.0001855024,0.000001166026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430099,0.001382829,0.8359467,0.0003287152,0.00004589153,0.0002162964,0.0004462914,0.001061642,0.01756174],"genre_scores_gemma":[0.82929,0.001006286,0.1618498,0.00006918491,0.00001822459,0.0003012545,0.000259751,0.0002114304,0.006993958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02147427,"threshold_uncertainty_score":0.04269856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05606760975264327,"score_gpt":0.3249832704043324,"score_spread":0.2689156606516891,"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."}}