{"id":"W3134332586","doi":"10.2118/204125-ms","title":"A Probabilistic Belief System to Track the Cleanliness of a Hole in Real-Time","year":2021,"lang":"en","type":"article","venue":"SPE/IADC International Drilling Conference and Exhibition","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Apache (Canada)","funders":"","keywords":"Probabilistic logic; Drilling; Bayesian network; Overhead (engineering); Computer science; Real-time computing; Track (disk drive); Process (computing); Key (lock); Simulation; Engineering; Artificial intelligence; Computer security; Mechanical 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":[0.0001844764,0.0001187184,0.0001683287,0.0001019252,0.00002959567,0.00005564654,0.0001049531,0.00005665582,0.0000271317],"category_scores_gemma":[0.00005354839,0.0001068163,0.0000381697,0.0001749932,0.000024135,0.00009108641,0.0000284404,0.0001168315,0.00002274672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006859227,"about_ca_system_score_gemma":0.00002691717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000546356,"about_ca_topic_score_gemma":0.00005288707,"domain_scores_codex":[0.9991835,0.00001931032,0.0002929411,0.0001791163,0.0001746752,0.0001504235],"domain_scores_gemma":[0.9995571,0.0000707471,0.0000307418,0.0001298582,0.0001668253,0.00004472796],"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":[0.00001950652,0.00003321692,0.0001789772,0.000317167,0.00003919029,0.00002309019,0.001159161,0.9322919,0.04209629,0.01878437,0.00007971318,0.004977444],"study_design_scores_gemma":[0.0002813364,0.00002357647,0.0016586,0.001072214,0.0000129658,0.00002196406,0.00043799,0.9843995,0.01075128,0.0007198643,0.0004407314,0.0001800071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9733372,0.00008171519,0.01668241,0.0002993344,0.0004890099,0.0001672952,0.00003493303,0.000150793,0.008757289],"genre_scores_gemma":[0.9988195,0.0001689859,0.0006479518,0.00001562125,0.0001441164,0.00001843558,0.00004732493,0.00001731339,0.0001207739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05210759,"threshold_uncertainty_score":0.4355841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01211050172184671,"score_gpt":0.2220643009545438,"score_spread":0.2099537992326971,"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."}}