{"id":"W4200389846","doi":"10.1016/j.mlwa.2021.100245","title":"Multistep networks for roll force prediction in hot strip rolling mill","year":2021,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Essar Steel Algoma (Canada); McGill University","funders":"Mitacs","keywords":"Mill; Process (computing); Rolling mill; Steel mill; Engineering; Strip steel; Stability (learning theory); Mechanical engineering; Computer science; Materials science; Metallurgy; Machine learning","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":[0.0006330295,0.0006644299,0.0006586667,0.0003832039,0.0003593466,0.0006481681,0.0007790386,0.0008273424,0.001271297],"category_scores_gemma":[0.001338742,0.0004738701,0.0003792502,0.0002660572,0.0003516311,0.000599735,0.0005039491,0.0009485838,0.0001931356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007371219,"about_ca_system_score_gemma":0.0006392353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01512469,"about_ca_topic_score_gemma":0.01212619,"domain_scores_codex":[0.9998335,0.0000387817,0.00001079074,0.00004931554,0.00003751524,0.0000300465],"domain_scores_gemma":[0.9993611,0.0003926445,0.00007888424,0.00002556628,0.000114998,0.00002682269],"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":[0.00005806851,0.00003328256,0.0006293871,0.00001406281,0.00001509139,0.00002228257,0.00001276091,0.9847493,0.0006727927,0.0003984968,0.0001395659,0.01325501],"study_design_scores_gemma":[6.371274e-7,0.000003939049,0.0000402933,4.249781e-7,6.896444e-7,5.097316e-7,6.884458e-7,0.9997848,0.00007374867,0.000082152,0.00001163581,5.523288e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2609056,0.0007998737,0.7328224,0.0003798741,0.00008401937,0.00006702691,0.0001882866,0.001271411,0.003481495],"genre_scores_gemma":[0.982466,0.00009450849,0.0159238,0.00003456475,0.00001501093,0.00003927684,0.00009922472,0.00001529969,0.001312296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01512469,"threshold_uncertainty_score":0.03007329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006460774580200423,"score_gpt":0.20653405277259,"score_spread":0.2000732781923896,"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."}}