{"id":"W3198154670","doi":"10.1063/5.0062775","title":"A novel framework for cost-effectively reconstructing the global flow field by super-resolution","year":2021,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Postdoctoral Science Foundation of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computational fluid dynamics; Physics; Flow (mathematics); Algorithm; Benchmark (surveying); Field (mathematics); Image resolution; Fluid dynamics; Resolution (logic); Mechanics; Computer science; Artificial intelligence; Optics; Mathematics; Geology; Geodesy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006104221,0.0007745598,0.0006593528,0.0006977079,0.0002653193,0.0005826431,0.001458716,0.0007595479,0.001400736],"category_scores_gemma":[0.001048352,0.0003712911,0.0007053729,0.0005512273,0.0005213567,0.001269448,0.0009461368,0.001046193,0.0003167949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005862782,"about_ca_system_score_gemma":0.001173899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008719154,"about_ca_topic_score_gemma":0.00834451,"domain_scores_codex":[0.9997566,0.000038427,0.00001269016,0.00005206044,0.0001119672,0.00002833097],"domain_scores_gemma":[0.9997689,0.00006826607,0.00003755732,0.00003554485,0.00006645309,0.00002326629],"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":[0.00009264104,0.00004480768,0.0008005758,0.000145199,0.00005528763,0.0001661191,0.00007055815,0.7688817,0.02412539,0.01898299,0.00211808,0.1845168],"study_design_scores_gemma":[0.000001617659,0.000006053968,0.00004141175,0.000002063016,0.000002632209,0.00001797146,0.00000218219,0.9974452,0.001031045,0.001021128,0.0004258631,0.000002788696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004761592,0.0001878639,0.9940937,0.00006577861,0.00001334627,0.00001391753,0.00003662908,0.0003268372,0.0005002763],"genre_scores_gemma":[0.2538473,0.0007972525,0.7417288,0.0001485124,0.00006861603,0.0001065967,0.0004333865,0.0002457759,0.002623806],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008719154,"threshold_uncertainty_score":0.01733679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02875400942799642,"score_gpt":0.2944703597292162,"score_spread":0.2657163503012198,"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."}}