{"id":"W3215901390","doi":"10.20944/preprints202111.0416.v1","title":"New Business and Operating Models. Optimization of a Blast Furnace in the Steel Industry. Machine Learning as a Process Optimization","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Iron and Steelmaking Processes","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Manufacturing engineering; Key (lock); Business intelligence; Computer science; Analytics; Industrial engineering; Manufacturing; Conceptual model; Artificial intelligence; Engineering; Knowledge management; Data science; Business; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004561294,0.0003576285,0.0004423318,0.0001864155,0.00009153701,0.0001254229,0.0004208649,0.0005097958,0.0002329256],"category_scores_gemma":[0.0003562437,0.0003355141,0.00004442269,0.0005667299,0.00003602984,0.0003204095,0.0004701677,0.001639679,0.000002438001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006515703,"about_ca_system_score_gemma":0.0002552264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003618642,"about_ca_topic_score_gemma":0.00006315359,"domain_scores_codex":[0.9982088,0.0001245309,0.0005487417,0.0005283143,0.0003364188,0.0002531713],"domain_scores_gemma":[0.998985,0.00005446317,0.0002130169,0.0004087871,0.000274796,0.00006396799],"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":[0.00001250918,0.00003606383,0.01640048,0.00116632,0.00004003902,0.000008185365,0.008700643,0.9731771,0.0001504609,0.0000599024,0.000001456012,0.0002468065],"study_design_scores_gemma":[0.0003988997,0.000008815454,0.003863082,0.0006531075,0.00006905715,0.00002225091,0.001547424,0.9911066,0.001916228,0.00004666765,0.00002739066,0.0003404324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7928253,0.001109676,0.1922858,0.000291728,0.0002112648,0.0008253439,0.000007423685,0.0002633016,0.0121802],"genre_scores_gemma":[0.9921826,0.0005132965,0.006383356,0.00004372134,0.00006045518,0.00007533503,0.00007479517,0.00007099399,0.0005955234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1993573,"threshold_uncertainty_score":0.9999097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05745078084893625,"score_gpt":0.2939876877678918,"score_spread":0.2365369069189555,"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."}}