{"id":"W886455110","doi":"10.1115/imece2011-64010","title":"Real Time Application of Bearing Wear Prediction Model Using Intelligent Drilling Advisory System","year":2011,"lang":"en","type":"article","venue":"Volume 7: Dynamic Systems and Control; Mechatronics and Intelligent Machines, Parts A and B","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drilling; Drilling engineering; Bearing (navigation); Bit (key); Engineering; Petroleum engineering; Oil field; Automotive engineering; Mechanical engineering; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004305158,0.0003138286,0.0005143641,0.0001680415,0.0001306071,0.00006411051,0.00008945422,0.000165074,0.000002284675],"category_scores_gemma":[0.000004788491,0.0002939194,0.00007204311,0.00008402677,0.0000423101,0.0001439509,0.00004370066,0.0001700873,0.000002694059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009763186,"about_ca_system_score_gemma":0.00001687651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006628598,"about_ca_topic_score_gemma":0.00001196114,"domain_scores_codex":[0.9984792,0.00003067492,0.0006439111,0.000350977,0.0001644686,0.0003308021],"domain_scores_gemma":[0.9994015,0.00002526159,0.000136198,0.0002269512,0.00005897518,0.0001511918],"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":[0.00004321021,0.00002974999,0.001738842,0.001234613,0.0002331582,0.00000154131,0.0009120826,0.960095,0.01366581,0.009827817,0.00000330024,0.01221486],"study_design_scores_gemma":[0.0002753335,0.00008721557,0.00008262189,0.000317748,0.0001308231,0.00003499455,0.0002364042,0.9981244,0.0001200982,0.0001265214,0.0002010735,0.0002627578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3020897,0.005416373,0.69133,0.000002066419,0.0002854681,0.0003826736,0.00004993453,0.0001931516,0.0002506438],"genre_scores_gemma":[0.996164,0.002683426,0.0008763435,0.000001975239,0.00007245697,0.00003664233,0.00001933111,0.00005949302,0.0000863671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6940743,"threshold_uncertainty_score":0.9999513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007850306804950389,"score_gpt":0.1858225077469643,"score_spread":0.177972200942014,"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."}}