{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005743218,0.0005322022,0.0005498698,0.00040679,0.0002460632,0.0005228646,0.0004029565,0.0004036323,0.001101538],"category_scores_gemma":[0.001590665,0.0002326001,0.0002270763,0.0002534068,0.0001457882,0.0003788859,0.0002474685,0.0003165851,0.0002588125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000263207,"about_ca_system_score_gemma":0.0004263863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01216615,"about_ca_topic_score_gemma":0.007132324,"domain_scores_codex":[0.999786,0.00004146414,0.0000175103,0.00004682042,0.00008434743,0.00002384053],"domain_scores_gemma":[0.9992771,0.0002575944,0.0000825443,0.00006733492,0.0002844919,0.0000310125],"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":[0.000787501,0.0002122087,0.01449237,0.0001424335,0.00006229946,0.0002619223,0.0002364333,0.7958829,0.02456431,0.0004740806,0.001797301,0.1610863],"study_design_scores_gemma":[0.00001072848,0.00005484433,0.001066075,0.000001726692,0.000006814966,0.00001277588,0.000008348406,0.9968954,0.001742862,0.00005336876,0.0001418832,0.000005192509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5228425,0.0001758955,0.4673092,0.0001602856,0.0001063712,0.00009560754,0.0001796389,0.005810377,0.003320055],"genre_scores_gemma":[0.9846699,0.00003537469,0.01431639,0.00001093036,0.000006995494,0.00002831055,0.00007515602,0.0000165935,0.0008402113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01216615,"threshold_uncertainty_score":0.02419066,"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."}}