{"id":"W4401583183","doi":"10.2139/ssrn.4925531","title":"Reducing Geological Uncertainty Through Coupled Flow-Geomechanics Based Surrogate Models and Rejection Sampling of Co2 Plume Prediction","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Geomechanics; Sampling (signal processing); Plume; Flow (mathematics); Geology; Environmental science; Computer science; Geotechnical engineering; Meteorology; Mechanics; Geography; 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":[0.001809902,0.0006340049,0.0008291387,0.0004422764,0.0004230746,0.000994838,0.001221315,0.001259122,0.0008726133],"category_scores_gemma":[0.008793718,0.0006249826,0.0006323559,0.0005353867,0.0007061189,0.001258161,0.00133283,0.001205033,0.000153048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005559087,"about_ca_system_score_gemma":0.001459882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008430347,"about_ca_topic_score_gemma":0.006207237,"domain_scores_codex":[0.9994105,0.0002790992,0.00002920329,0.0000955123,0.0001090836,0.00007661458],"domain_scores_gemma":[0.9967985,0.002034475,0.0003331135,0.0003021577,0.0004019116,0.0001300479],"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":[0.0001050814,0.00005517227,0.001507615,0.0000130258,0.00001776824,0.00002135782,0.00001729829,0.9866723,0.001228477,0.002691962,0.0001033375,0.007566643],"study_design_scores_gemma":[0.000003431539,0.000004738036,0.00008111887,5.871159e-7,0.000001022473,0.000001241069,7.547795e-7,0.9991705,0.0001915035,0.0005291108,0.00001470037,0.000001361342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2004815,0.00008779836,0.7974131,0.0002291427,0.0000406871,0.00003124775,0.0001400117,0.0004035256,0.001173045],"genre_scores_gemma":[0.9593461,0.00003344079,0.03981495,0.00004475478,0.00002028097,0.00003907884,0.0002038843,0.00004319935,0.0004543445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008430347,"threshold_uncertainty_score":0.01676255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0282852133538075,"score_gpt":0.2741684728807814,"score_spread":0.2458832595269739,"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."}}