{"id":"W2770505324","doi":"10.1002/srin.201700074","title":"Review of Modeling and Simulation of Galvanizing Operations","year":2017,"lang":"en","type":"article","venue":"steel research international","topic":"Metallurgical Processes and Thermodynamics","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Polytechnique Montréal","funders":"","keywords":"Dross; Galvanization; Coating; Materials science; Metallurgy; Computer simulation; Zinc; Turbulence; Intermetallic; Process engineering; Flow (mathematics); Mechanical engineering; Mechanics; Engineering; Composite material; Layer (electronics); Simulation; Alloy","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":[],"consensus_categories":[],"category_scores_codex":[0.0004871054,0.00003626693,0.00008629429,0.00005636133,0.00006752667,0.00002949262,0.0001985565,0.00002008006,0.00009592699],"category_scores_gemma":[0.0005603732,0.00003192041,0.0000218988,0.00002954174,0.00004851726,0.0001694173,0.00007431059,0.00009435647,0.000002056395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001589855,"about_ca_system_score_gemma":0.00001478944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002337555,"about_ca_topic_score_gemma":0.000008738519,"domain_scores_codex":[0.9994194,0.00001400599,0.0001711722,0.00006335064,0.000262117,0.00006996984],"domain_scores_gemma":[0.9993569,0.00005656388,0.00002111261,0.0001387904,0.0004004113,0.00002627216],"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":[0.000004295098,0.00001066239,0.00002279186,0.001492109,0.00003293271,4.224148e-7,0.00003101813,0.9799488,0.002535822,0.01274514,0.0000128004,0.003163225],"study_design_scores_gemma":[0.00007847247,0.000007803741,0.000101439,0.001018381,0.000002372664,4.553114e-7,0.000009265732,0.9972473,0.00009459309,0.0009424611,0.0004703694,0.00002711165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.518915,0.02153986,0.3560362,0.0008061972,0.0004948397,0.0005755965,0.00007545864,0.00005003028,0.1015069],"genre_scores_gemma":[0.9906949,0.00867383,0.0005083647,0.000006183794,0.00003582068,0.000004168923,0.000006566603,0.000006391022,0.00006380047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4717799,"threshold_uncertainty_score":0.1301677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1237658334540551,"score_gpt":0.4232825615089926,"score_spread":0.2995167280549375,"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."}}