{"id":"W3034752634","doi":"10.1063/5.0008889","title":"Flow field reconstruction and prediction of the supersonic cascade channel based on a symmetry neural network under complex and variable conditions","year":2020,"lang":"en","type":"article","venue":"AIP Advances","topic":"Nuclear Engineering Thermal-Hydraulics","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Guelph","keywords":"Cascade; Extrapolation; Artificial neural network; Computer science; Algorithm; Channel (broadcasting); Supersonic speed; Flow (mathematics); Convolution (computer science); Test set; Artificial intelligence; Mathematics; Geometry; Mechanics; Mathematical analysis; Physics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003093511,0.00008294325,0.00009740415,0.00001907657,0.00005784123,0.00001089392,0.00004595908,0.00004395645,0.00002427748],"category_scores_gemma":[0.00001965077,0.00007334434,0.00001839466,0.0001402469,0.00003588348,0.0001110122,0.00001366792,0.0001315935,7.235406e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000138622,"about_ca_system_score_gemma":0.000004107419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003214924,"about_ca_topic_score_gemma":0.000003415639,"domain_scores_codex":[0.9996236,0.00001546134,0.00009435554,0.00009964525,0.00005849843,0.0001084465],"domain_scores_gemma":[0.9997587,0.00009171903,0.00001752681,0.0000830315,0.000008860428,0.00004013243],"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":[0.000008259578,0.000003072252,0.0003743377,0.00006936635,0.00001470091,2.735656e-7,0.00005982895,0.9956875,0.001880432,0.0002394411,0.000335347,0.00132745],"study_design_scores_gemma":[0.0002001377,0.0000686721,0.006990411,0.00004807979,0.00001656289,0.00001010018,0.00005871338,0.9910496,0.0004063399,0.0002961325,0.0007903571,0.00006482737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9265359,0.002086786,0.0612312,0.002939638,0.002005347,0.0005646618,0.0001808104,0.000683162,0.003772493],"genre_scores_gemma":[0.9979327,0.00007566207,0.001427241,0.0004157618,0.0001194115,0.000005007222,0.000005020268,0.00001683053,0.000002324662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07139684,"threshold_uncertainty_score":0.2990896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01182730883455644,"score_gpt":0.1925242142682056,"score_spread":0.1806969054336492,"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."}}