{"id":"W4362648728","doi":"10.2139/ssrn.4410411","title":"Label-Free Ai-Based Surrogate Modelling for Highly Compressible Subsurface Flow Using Both Physics and Non-Physics Regularizations","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Lattice Boltzmann Simulation Studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Physics; Flow (mathematics); Compressibility; Compressible flow; Statistical physics; Theoretical physics; Mechanics","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007592195,0.0005200297,0.0006382953,0.00013597,0.0005398557,0.0002778266,0.0004041197,0.0002608137,0.000001193298],"category_scores_gemma":[0.0000322641,0.0005689209,0.000202185,0.0003007223,0.00006660034,0.0002807512,0.0002652224,0.002392114,0.000003950061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008809414,"about_ca_system_score_gemma":0.001168093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000440672,"about_ca_topic_score_gemma":0.0001292186,"domain_scores_codex":[0.9967592,0.00005431176,0.0005712337,0.0004443072,0.0003593944,0.001811584],"domain_scores_gemma":[0.9985356,0.0002263297,0.0002637898,0.0005014513,0.0003671483,0.000105702],"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":[0.00002017328,0.00002897342,0.0002048348,0.0002607562,0.0006008835,0.000001137595,0.0001536552,0.99428,0.0002540664,0.003613184,0.00008208286,0.0005001876],"study_design_scores_gemma":[0.001265715,0.00003019808,0.00002172369,0.0002005727,0.0002794254,0.000004007712,0.00006498429,0.8006085,0.000252587,0.1967784,0.00005640484,0.0004374708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04010411,0.001506138,0.9564316,0.0001988454,0.00065282,0.0005411485,0.0001807706,0.0003529705,0.0000315407],"genre_scores_gemma":[0.9434497,0.002057623,0.05237858,0.00004244661,0.001090021,0.00005195069,0.000183726,0.0004210337,0.0003249383],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9040531,"threshold_uncertainty_score":0.9999094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04651306531544442,"score_gpt":0.2786262168526599,"score_spread":0.2321131515372155,"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."}}