{"id":"W2292669995","doi":"10.5555/2888619.2889075","title":"Updating geological conditions using bayes theorem and markov chain","year":2015,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Tunneling and Rock Mechanics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Markov chain; Borehole; Computer science; Bayes' theorem; Section (typography); Mathematical optimization; Operations research; Data mining; Industrial engineering; Geology; Bayesian probability; Engineering; Machine learning; Artificial intelligence; Mathematics; Geotechnical 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":[0.0001296432,0.00009339937,0.00009807077,0.00004450241,0.00006125898,0.00004322095,0.00005561479,0.00006484192,0.00008757905],"category_scores_gemma":[0.00008549908,0.00008461133,0.00001649289,0.00005102837,0.00002029337,0.0001048408,0.00003818775,0.0000973248,0.00001131318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000211675,"about_ca_system_score_gemma":0.00001245896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003837163,"about_ca_topic_score_gemma":0.000003626847,"domain_scores_codex":[0.9995035,0.00003088625,0.0001429824,0.0001205241,0.00007673634,0.0001253798],"domain_scores_gemma":[0.9996449,0.00007948645,0.00002318846,0.00009299479,0.00008314804,0.00007627564],"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":[0.000007700914,0.00000848716,0.0009602853,0.00001648915,0.00001627767,0.000003243027,0.001056396,0.9833081,0.001681228,0.01006202,0.00007788948,0.002801926],"study_design_scores_gemma":[0.0001949649,0.00001465577,0.0002799407,0.00003910433,0.00000767691,0.00000615105,0.0003077598,0.9940401,0.0002580958,0.004500934,0.0002398866,0.0001107578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5320711,0.00004656756,0.4658859,0.00004218407,0.0002175259,0.00005730548,0.000007340077,0.0001875609,0.001484519],"genre_scores_gemma":[0.9974172,0.000002930713,0.002381878,0.00004457665,0.00007512322,0.000002234044,0.000013975,0.00001113194,0.00005095004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4653461,"threshold_uncertainty_score":0.345035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05108450907430459,"score_gpt":0.2792760087969668,"score_spread":0.2281914997226623,"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."}}