{"id":"W2869144014","doi":"10.3390/ma11071179","title":"Toward Better Control of Inclusion Cleanliness in a Gas Stirred Ladle Using Multiscale Numerical Modeling","year":2018,"lang":"en","type":"article","venue":"Materials","topic":"Metallurgical Processes and Thermodynamics","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Ladle; Inclusion (mineral); Non-metallic inclusions; Settling; Materials science; Mechanics; Particle size; Population; Particle-size distribution; Multiscale modeling; Metallurgy; Environmental science; Chemistry; Engineering; Chemical engineering; Mineralogy; Physics; Environmental engineering","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":[0.000415914,0.0004761551,0.0007009289,0.0003443158,0.0004138452,0.000969831,0.000623771,0.001030101,0.0007766922],"category_scores_gemma":[0.0008210306,0.0003127473,0.0006838936,0.0002284024,0.0007524007,0.0005514788,0.0007796792,0.0005062582,0.0000987825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008616894,"about_ca_system_score_gemma":0.0008903862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007165129,"about_ca_topic_score_gemma":0.003474199,"domain_scores_codex":[0.9998678,0.00003224208,0.000008467266,0.00002819637,0.00003809574,0.00002519284],"domain_scores_gemma":[0.9996812,0.0001532991,0.0000677108,0.00002621303,0.00003730209,0.00003432061],"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":[0.00004037064,0.00005262313,0.000864864,0.00004802219,0.00001796838,0.00007886968,0.00004428498,0.9614121,0.02940102,0.006258297,0.00008713958,0.001694398],"study_design_scores_gemma":[0.00000420873,0.000008237246,0.000080837,0.000001497565,0.000001946263,0.000002270217,0.000002617343,0.9987219,0.000836856,0.0002343267,0.0001028376,0.000002565904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6210793,0.0007248729,0.3659225,0.0005976564,0.00009393546,0.0001321847,0.0002381014,0.0003855966,0.0108258],"genre_scores_gemma":[0.9689398,0.0003125521,0.02879984,0.0000585636,0.00001984943,0.0001160147,0.00009087842,0.00003556143,0.001626896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007165129,"threshold_uncertainty_score":0.01424688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456804530614582,"score_gpt":0.2484974716882938,"score_spread":0.223929426382148,"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."}}