{"id":"W4402438599","doi":"10.11159/htff24.225","title":"Development of Numerical Model Based Deep Learning for the Temperature Prediction of the Hot Rolling Process","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Computer science; Artificial intelligence; Deep learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005175159,0.0008387899,0.0008020927,0.000460609,0.0003016703,0.0008147578,0.001680754,0.001032577,0.002131489],"category_scores_gemma":[0.001134605,0.0004737466,0.0007538717,0.000529144,0.0003775006,0.001035929,0.0007871797,0.001765381,0.0005922435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009077711,"about_ca_system_score_gemma":0.001173784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01212925,"about_ca_topic_score_gemma":0.009713567,"domain_scores_codex":[0.9998062,0.00002730223,0.00001295608,0.0000577217,0.00006755883,0.0000281647],"domain_scores_gemma":[0.9997345,0.00009884838,0.00003159302,0.00001762117,0.00009676044,0.00002071953],"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":[0.00003998326,0.00006191441,0.001041283,0.00008442005,0.00005115332,0.00005147243,0.00002820161,0.9086834,0.002720837,0.004553736,0.001585022,0.08109867],"study_design_scores_gemma":[7.733223e-7,0.000004091464,0.00002504629,0.000001752697,0.00000138452,0.000001858101,8.06668e-7,0.9993269,0.0001473476,0.000363817,0.0001251015,0.000001086331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01175616,0.001145888,0.9831442,0.0002815784,0.00008574938,0.00003297419,0.0001432974,0.001011643,0.002398508],"genre_scores_gemma":[0.6770008,0.001956939,0.3108609,0.0004714075,0.0001395875,0.0003180605,0.0009785898,0.0002093319,0.008064308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01212925,"threshold_uncertainty_score":0.02411729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007783335923913567,"score_gpt":0.2019313596898645,"score_spread":0.194148023765951,"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."}}