{"id":"W4416596040","doi":"10.1007/s10845-025-02736-9","title":"Data-driven decision support for furnace loading in steel tempering: a machine learning approach based on industrial operations","year":2025,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Metallurgical Processes and Thermodynamics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cégep de Sorel-Tracy; École de Technologie Supérieure","funders":"Mitacs","keywords":"Forging; Tempering; Process (computing); Boosting (machine learning); Technician; Feature (linguistics); Production planning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007169642,0.0002010777,0.0003662509,0.0004733428,0.00008836478,0.0001144517,0.0005017982,0.0001200545,0.00006423965],"category_scores_gemma":[0.0002492742,0.0001630212,0.0001201494,0.0001544892,0.00001274969,0.0002409255,0.0000823933,0.0007471053,0.000002955743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000202039,"about_ca_system_score_gemma":0.00006895251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005819029,"about_ca_topic_score_gemma":0.00001934957,"domain_scores_codex":[0.9985265,0.00003102736,0.000768768,0.0001942391,0.0002400434,0.0002394728],"domain_scores_gemma":[0.9992432,0.0002691833,0.0001045504,0.0002342556,0.00006421177,0.00008461715],"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":[0.0001484812,0.00008547785,0.0000261367,0.00009714425,0.00005743733,0.00001143025,0.00005230053,0.9846401,0.0004754419,0.00009691682,0.00006616219,0.01424296],"study_design_scores_gemma":[0.0008164142,0.0001165694,0.00002548943,0.0002894937,0.00003342814,0.00001069591,0.0001083401,0.9762959,0.006715086,0.00006496472,0.01537955,0.0001440717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2126826,0.0001178968,0.7851369,0.0000616014,0.0005115774,0.0002446977,0.00001566107,0.00003263097,0.001196401],"genre_scores_gemma":[0.9878442,0.0001252798,0.01165462,0.00005361714,0.0001353101,0.000006590144,0.00003638873,0.000029961,0.0001140245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7751616,"threshold_uncertainty_score":0.664781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04843323893099497,"score_gpt":0.2864456362694937,"score_spread":0.2380123973384987,"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."}}