{"id":"W2928373814","doi":"10.1109/tcyb.2019.2901268","title":"Granular Prediction and Dynamic Scheduling Based on Adaptive Dynamic Programming for the Blast Furnace Gas System","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Scheduling (production processes); Dynamic priority scheduling; Computer science; Mathematical optimization; Schedule; Mathematics","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":[0.000620171,0.0007130057,0.0008383819,0.0003670414,0.0004382137,0.0008186357,0.0006343597,0.0006260065,0.0009000393],"category_scores_gemma":[0.001465021,0.0003323935,0.0004833433,0.0004205618,0.0005919568,0.0005880013,0.0007074596,0.0009948025,0.00008031086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007883512,"about_ca_system_score_gemma":0.0009527135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01453604,"about_ca_topic_score_gemma":0.006814234,"domain_scores_codex":[0.9997033,0.00006788292,0.00001646055,0.00007458012,0.00008695666,0.0000507189],"domain_scores_gemma":[0.9995168,0.0002815114,0.00006910758,0.00002254314,0.00008307474,0.00002693114],"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":[0.00003041025,0.00001514432,0.0002577843,0.00001981649,0.00000990125,0.00003228932,0.00002505287,0.9849722,0.0006809999,0.002060673,0.0001317637,0.01176396],"study_design_scores_gemma":[0.000001626482,0.000005012386,0.00002957922,6.481708e-7,0.000001238538,0.000001710312,0.000001288316,0.9995277,0.00007149155,0.0003295581,0.00002895862,0.000001237109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04015985,0.0002345864,0.956714,0.0001709423,0.00003609159,0.00004384902,0.00002582771,0.0002362566,0.002378647],"genre_scores_gemma":[0.9544113,0.0001422793,0.04404356,0.00004234826,0.00002151093,0.00009830418,0.00004023364,0.00002392989,0.001176495],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01453604,"threshold_uncertainty_score":0.02890289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007921224045344375,"score_gpt":0.2191847700500824,"score_spread":0.211263546004738,"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."}}