{"id":"W4391857915","doi":"10.33737/gpps23-tc-146","title":"Improving an Algorithm for Assessing the Completion of Internal, Unsteady Flow Simulations using Spatial Downsampling","year":2023,"lang":"en","type":"article","venue":"Proceedings","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"LightMachinery (Canada)","funders":"Alliance de recherche numérique du Canada","keywords":"Computational fluid dynamics; Upsampling; Computer science; Algorithm; Convergence (economics); Flow (mathematics); Computation; Reduction (mathematics); Mathematical optimization; Discretization; Artificial intelligence; Mathematics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002880592,0.000859113,0.0007949955,0.001087992,0.0005263765,0.0009558999,0.001307308,0.0008845916,0.001466625],"category_scores_gemma":[0.01039052,0.0004996067,0.0006389338,0.0005413556,0.0005884806,0.001092972,0.001063448,0.001371162,0.0005245156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008614824,"about_ca_system_score_gemma":0.00204885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007767321,"about_ca_topic_score_gemma":0.008351916,"domain_scores_codex":[0.9989895,0.000214938,0.00008607015,0.0001765317,0.0004678234,0.00006508282],"domain_scores_gemma":[0.9963962,0.001365057,0.0002969392,0.0004207858,0.001431644,0.00008935964],"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":[0.0002688585,0.0002077429,0.00482411,0.0001714948,0.0001002355,0.0001125013,0.0002649815,0.5110545,0.04075045,0.009199984,0.002948699,0.4300964],"study_design_scores_gemma":[0.000008218015,0.00002394385,0.000318749,0.000004363947,0.000004226634,0.00001803425,0.00001016977,0.9928569,0.005330973,0.0007363835,0.0006799842,0.000008095592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01277149,0.00004605296,0.9856635,0.00005015533,0.00003065472,0.00006323713,0.00002278424,0.0009974889,0.0003545464],"genre_scores_gemma":[0.06583482,0.00004447869,0.9331563,0.0000384156,0.00001537325,0.0001090169,0.0001451176,0.0001910427,0.0004653798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007767321,"threshold_uncertainty_score":0.01544422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06053347545078639,"score_gpt":0.3405746036354139,"score_spread":0.2800411281846275,"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."}}