{"id":"W4312344404","doi":"10.1115/gt2022-80013","title":"Particle Rebound/Deposition Modelling in Engine Hot Sections","year":2022,"lang":"en","type":"article","venue":"","topic":"Particle Dynamics in Fluid Flows","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence; National Research Council Canada","funders":"","keywords":"Deposition (geology); Particle deposition; Particle (ecology); Nozzle; Range (aeronautics); Mechanics; Calibration; Materials science; Particle size; Computational fluid dynamics; Environmental science; Meteorology; Physics; Geology; Thermodynamics; Composite material","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":[0.000163544,0.00007437393,0.00007487703,0.00007309788,0.0001079745,0.00002169708,0.00008323123,0.00002019593,0.0002272051],"category_scores_gemma":[0.000003614463,0.00009398985,0.00002720023,0.0003206243,0.000009061714,0.0001137254,0.00004022507,0.0002133579,0.00003653118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002535228,"about_ca_system_score_gemma":0.000006767452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005967657,"about_ca_topic_score_gemma":0.00008117484,"domain_scores_codex":[0.9993168,0.00002520807,0.0001840004,0.0001176754,0.0001294593,0.0002268534],"domain_scores_gemma":[0.9997508,0.00002720742,0.000006963759,0.0001649134,0.000008677987,0.00004143352],"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":[0.000004843886,0.00003222217,0.0002802938,0.000004690874,0.000006579189,0.000007974051,0.0002329894,0.9860569,0.01005692,0.002888581,0.00006468633,0.0003633153],"study_design_scores_gemma":[0.0002041453,0.0000172889,0.0001859573,0.00000192329,0.000003779186,0.00001362565,0.0001123705,0.9946696,0.003891508,0.0005360915,0.0002505308,0.0001132186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8799399,0.0001095221,0.1162245,0.00004373434,0.0002797016,0.00009088022,0.000005866378,0.0003647353,0.002941156],"genre_scores_gemma":[0.9970294,0.00001304424,0.002610176,0.00003094496,0.00002744747,0.0001068345,0.000008271164,0.00002428669,0.0001496537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1170894,"threshold_uncertainty_score":0.3832795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399921799019531,"score_gpt":0.2012327147169927,"score_spread":0.1872334967267974,"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."}}