{"id":"W2959213384","doi":"10.35840/2631-5068/6519","title":"Electrodynamic Concentration of Non-ferrous Metallic Particles in the Moving Gas-powder Stream: Mathematical Modeling and Analysis","year":2019,"lang":"en","type":"article","venue":"International Journal of Magnetics and Electromagnetism","topic":"Aerosol Filtration and Electrostatic Precipitation","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"RADIUS; Generator (circuit theory); Metal; Particle size; Analytical Chemistry (journal); Physics; Materials science; Particle (ecology); Metal powder; Atomic physics; Nanotechnology; Chemistry; Metallurgy; Thermodynamics; Chromatography; Physical chemistry; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000321595,0.0001025446,0.0002119226,0.0001545974,0.00001594527,0.00006935022,0.0001404623,0.00004462971,0.00007695954],"category_scores_gemma":[0.00003083754,0.00008265225,0.00005649561,0.000183292,0.00002654654,0.0001719819,0.00001027389,0.0001682791,0.000001698194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002954571,"about_ca_system_score_gemma":0.00002226905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009533338,"about_ca_topic_score_gemma":0.00002222686,"domain_scores_codex":[0.9989358,0.00004255409,0.0004702336,0.0000865911,0.0003106755,0.0001541589],"domain_scores_gemma":[0.999548,0.0001097155,0.0001156656,0.00007068453,0.0001206244,0.00003526313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001873515,0.0002005717,0.004373868,0.0001041831,0.0009688933,0.00002426397,0.005098768,0.1284505,0.8194672,0.009032674,0.00006955888,0.03202216],"study_design_scores_gemma":[0.0005810565,0.0005273093,0.008113096,0.00002515469,0.0001590993,0.0000674625,0.0001534695,0.9825532,0.001919333,0.005799076,0.000008029966,0.00009375669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841467,0.0008614919,0.01409865,0.0003976813,0.00004536899,0.00009920253,0.000002551123,0.000005842377,0.0003424792],"genre_scores_gemma":[0.9974494,0.0008247884,0.001619573,0.0000437515,0.00002546968,0.000002816258,0.000007771121,0.000008702932,0.00001774369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8541027,"threshold_uncertainty_score":0.3370461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004762501384202172,"score_gpt":0.2194075570913417,"score_spread":0.2146450557071395,"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."}}