{"id":"W4388984248","doi":"10.32339/0135-5910-2021-12-1227-1238","title":"FORECASTING OF INDUSTRIAL COKE QUALITY AT JSC EVRAZ NTMK BASED ON DATA OF PASSIVE INDUSTRIAL EXPERIMENT","year":2022,"lang":"en","type":"article","venue":"Ferrous Metallurgy Bulletin of Scientific Technical and Economic Information","topic":"Coal and Coke Industries Research","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"EVRAZ (Canada)","funders":"","keywords":"Coke; Quenching (fluorescence); Process engineering; Quality (philosophy); Environmental science; Metallurgy; Engineering; Waste management; Materials science; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00264349,0.000168722,0.0004742077,0.0003171762,0.0003357622,0.00005271989,0.0007307071,0.0001825349,0.004228969],"category_scores_gemma":[0.0006557533,0.0001591924,0.0001138599,0.0002331212,0.000476946,0.0001888429,0.001421532,0.0003873166,0.00002522449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002689071,"about_ca_system_score_gemma":0.0002539998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00210985,"about_ca_topic_score_gemma":0.00006886536,"domain_scores_codex":[0.9971972,0.0001908241,0.001381575,0.0003414444,0.0006006309,0.0002883839],"domain_scores_gemma":[0.9977621,0.0003702783,0.0009137936,0.0007446751,0.0001094206,0.00009974222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0157556,0.001971725,0.001799843,0.0003975301,0.0004837767,0.000009034523,0.001986442,0.2160467,0.00846267,0.02355632,0.1964343,0.5330961],"study_design_scores_gemma":[0.005526662,0.001311566,0.0002442204,0.0001072452,0.00006424375,0.00001484525,0.002312247,0.08767977,0.02823023,0.0001764161,0.8738283,0.0005042417],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873045,0.00001706495,0.00004424213,0.0006310844,0.0008160109,0.0004865615,0.0007270238,0.00003713221,0.009936349],"genre_scores_gemma":[0.998508,0.000002343334,0.0001052838,0.00003284245,0.00007125843,0.00006123343,0.0007317284,0.000008332946,0.0004789705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.677394,"threshold_uncertainty_score":0.9966813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1547529623344638,"score_gpt":0.2965658853134737,"score_spread":0.14181292297901,"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."}}