{"id":"W4405469530","doi":"10.1190/image2024-4099652.1","title":"Kriging-based surrogate models for convergence acceleration of Markov chains: An example of magnetotellurics-dix joint inversion","year":2024,"lang":"en","type":"article","venue":"","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Magnetotellurics; Inversion (geology); Acceleration; Kriging; Markov chain; Convergence (economics); Joint (building); Geology; Markov process; Computer science; Algorithm; Mathematics; Statistics; Seismology; Machine learning; Engineering; Electrical engineering; Physics; Electrical resistivity and conductivity; Structural engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0004671607,0.0001243249,0.0001889284,0.0002015338,0.00001921127,0.00002303865,0.00009500016,0.00007640016,0.0001158784],"category_scores_gemma":[0.0000218998,0.0001200148,0.0000826172,0.0002369946,0.00001646386,0.0002800984,0.00001094125,0.00006874991,0.000001718848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002829091,"about_ca_system_score_gemma":0.00002935045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001338375,"about_ca_topic_score_gemma":0.000009892219,"domain_scores_codex":[0.9991717,0.00002913662,0.0003308155,0.0001694106,0.0001522547,0.0001466662],"domain_scores_gemma":[0.9994454,0.000152386,0.00002530917,0.000225761,0.00009166145,0.00005952946],"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":[0.00001090276,0.00001058358,0.00001371938,0.0007942179,0.00001449226,4.309921e-7,0.0002065234,0.9730567,0.02004824,0.003074189,0.0001594387,0.002610585],"study_design_scores_gemma":[0.0002485776,0.00007012925,0.00002841762,0.00005601626,0.00001086887,2.024628e-7,0.00002711158,0.903963,0.0944147,0.0005140748,0.0005547261,0.0001121826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2865522,0.0001761736,0.7122885,0.00001325794,0.0002504614,0.0001697377,0.00001268546,0.0002165515,0.0003204253],"genre_scores_gemma":[0.8978366,0.00004200628,0.1017677,0.000006887559,0.00003179097,0.00001512747,0.00004110993,0.000030553,0.0002282937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6112844,"threshold_uncertainty_score":0.489406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08174724434046944,"score_gpt":0.2840946529481119,"score_spread":0.2023474086076425,"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."}}