{"id":"W3006393566","doi":"10.1093/gji/ggaa072","title":"Hydrogeophysical data integration through Bayesian Sequential Simulation with log-linear pooling","year":2020,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Pooling; Variable (mathematics); Computer science; Bayesian probability; Independence (probability theory); Random variable; Algorithm; Mathematical optimization; Mathematics; Statistics; Artificial intelligence","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":[0.002518953,0.0006891832,0.00098836,0.0007105106,0.0004594469,0.001121738,0.001665374,0.0008018192,0.001552937],"category_scores_gemma":[0.006603233,0.0008112955,0.0009673273,0.0008347283,0.0008480844,0.001475311,0.001532895,0.000923859,0.0002865996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138219,"about_ca_system_score_gemma":0.00171597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0144258,"about_ca_topic_score_gemma":0.0104844,"domain_scores_codex":[0.9991905,0.0003545858,0.00004895199,0.0001424868,0.0001939913,0.00006939887],"domain_scores_gemma":[0.9972382,0.001775151,0.0002742409,0.0002457126,0.0003559431,0.0001108254],"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":[0.0000416166,0.00001981166,0.0007741551,0.0000139541,0.00002773577,0.00003420612,0.00003441816,0.9829016,0.0008817257,0.002728851,0.0001502896,0.0123918],"study_design_scores_gemma":[0.000001694474,0.000002859751,0.0000342485,8.80601e-7,0.000001644594,0.000002085545,0.000001254505,0.9989384,0.0001683773,0.0008011215,0.00004501146,0.000002415332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03134589,0.00005143459,0.9671454,0.00009728091,0.00001133905,0.00003093453,0.00004883444,0.0005887753,0.00068011],"genre_scores_gemma":[0.767276,0.00007792041,0.2309103,0.000109025,0.00002713797,0.0001728414,0.0002885454,0.0001731449,0.0009649819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0144258,"threshold_uncertainty_score":0.02868372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06263349893161561,"score_gpt":0.3288411312255992,"score_spread":0.2662076322939835,"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."}}