{"id":"W2017396704","doi":"10.2118/2004-047","title":"Direct Prediction of Reservoir Performance With Bayesian Updating Under a Multivariate Gaussian Model","year":2004,"lang":"en","type":"article","venue":"Canadian International Petroleum Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Citation; Multivariate statistics; Computer science; Bayesian probability; Data mining; Information retrieval; Machine learning; Operations research; Artificial intelligence; Library science; 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":[0.0001455684,0.0001554282,0.000154092,0.0003056083,0.00005797082,0.0000498837,0.0002582837,0.00007568981,0.00006723998],"category_scores_gemma":[0.00003555117,0.0001537013,0.00003401909,0.0001401224,0.00003672768,0.000276359,0.00001284385,0.0001825553,0.000005413079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003702126,"about_ca_system_score_gemma":0.0003486762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004895922,"about_ca_topic_score_gemma":0.007435992,"domain_scores_codex":[0.9990627,0.00001434304,0.0002499943,0.0001788472,0.000244184,0.0002499705],"domain_scores_gemma":[0.9993657,0.00002658975,0.00004392587,0.0002035864,0.000149053,0.0002111364],"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":[0.00001067787,0.000004902268,0.001415199,0.00002637346,0.00004867485,0.000002656664,0.0001349999,0.9928884,0.0007564392,0.004617821,0.00001857628,0.00007527529],"study_design_scores_gemma":[0.0005906834,0.00003004028,0.008584237,0.0001452828,0.000007837005,0.000005533787,0.00005200114,0.9886444,0.00121214,0.0002472637,0.0003390134,0.0001415852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6985881,0.00001249245,0.2635988,0.0001923236,0.0001927832,0.00005843283,0.0001061111,0.0001065288,0.03714445],"genre_scores_gemma":[0.9821895,0.00001765879,0.0173238,0.00001839654,0.0000558922,0.00001898174,0.00005719558,0.00003054497,0.0002880547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2836014,"threshold_uncertainty_score":0.7401202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209092123849982,"score_gpt":0.2425905547115428,"score_spread":0.220499633473043,"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."}}