{"id":"W4392846839","doi":"10.2139/ssrn.4760353","title":"Towards Time-Series Prediction of Cavitating Fluid Flow with Graph Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Series (stratigraphy); Artificial neural network; Computer science; Graph; Cavitation; Flow (mathematics); Time series; Mathematics; Artificial intelligence; Machine learning; Geology; Theoretical computer science; Mechanics; Geometry; Physics","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":[0.0006253768,0.0009816941,0.0008411927,0.0009390102,0.0002997721,0.0007671429,0.0009949579,0.001400516,0.00122282],"category_scores_gemma":[0.003693692,0.0005505804,0.0008328322,0.0009615357,0.0005697205,0.0008990433,0.000631079,0.001658816,0.0005058166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006824603,"about_ca_system_score_gemma":0.0006662615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02024255,"about_ca_topic_score_gemma":0.01310282,"domain_scores_codex":[0.9998088,0.0000643123,0.000009919236,0.00006493401,0.00002697354,0.00002510389],"domain_scores_gemma":[0.998312,0.001248927,0.0001214231,0.00008541196,0.0001830426,0.00004924819],"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":[0.00003863326,0.00002670778,0.0004441573,0.00002389895,0.00003196542,0.00001921148,0.00001332174,0.9724174,0.0008306082,0.001451132,0.0004899953,0.02421298],"study_design_scores_gemma":[4.52452e-7,0.000001427042,0.00002703335,5.467803e-7,8.15287e-7,5.068691e-7,4.743849e-7,0.9995226,0.00003687291,0.0003938408,0.00001486149,5.322622e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08393751,0.000755981,0.9122626,0.0003254097,0.00009561121,0.00003310682,0.0002417443,0.001215575,0.001132401],"genre_scores_gemma":[0.885659,0.000469137,0.1095988,0.0001129776,0.0001128628,0.00007509925,0.0006177255,0.0001390069,0.003215454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02024255,"threshold_uncertainty_score":0.04024941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004369147898967516,"score_gpt":0.1972147811482351,"score_spread":0.1928456332492676,"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."}}