{"id":"W1973995224","doi":"10.2118/136006-ms","title":"Real-Time Bit Wear Optimization Using the Intelligent Drilling Advisory System","year":2010,"lang":"en","type":"article","venue":"SPE Russian Oil and Gas Conference and Exhibition","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Drilling; Drill bit; Bit (key); Software; Rate of penetration; Drilling engineering; Offset (computer science); Computer science; Simulation; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.00014611,0.0001543659,0.0001432753,0.0000726302,0.0001551907,0.0001331298,0.0000501575,0.00009911788,0.00003157019],"category_scores_gemma":[0.000005139036,0.0001222953,0.00002766983,0.000087108,0.00004846196,0.0001417817,0.00001779711,0.0001923964,0.00001099427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001834273,"about_ca_system_score_gemma":0.00001210877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004814028,"about_ca_topic_score_gemma":0.000008344682,"domain_scores_codex":[0.9993608,0.00001400545,0.0001765837,0.0001643971,0.00008854506,0.0001956732],"domain_scores_gemma":[0.9997112,0.00002280697,0.00002943389,0.0001316193,0.00002029476,0.00008461271],"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":[0.00001470484,0.00001379448,0.0001283853,0.0006214282,0.00005035878,0.00001087937,0.001596064,0.8270995,0.07212948,0.01409762,0.00003464797,0.08420313],"study_design_scores_gemma":[0.0001058147,0.00001301699,0.00004061474,0.0002753252,0.00002477856,0.00003916386,0.0002732421,0.9952151,0.003371631,0.0001563982,0.0003111169,0.0001738015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7788804,0.0004396116,0.190705,0.000414075,0.001003557,0.0001263418,0.00001486506,0.0007545459,0.02766157],"genre_scores_gemma":[0.9931483,0.002944832,0.00352878,0.000008336459,0.000205958,0.000005154689,0.00001602375,0.000026944,0.0001156179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2142679,"threshold_uncertainty_score":0.4987056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028370327776181,"score_gpt":0.1977145551278364,"score_spread":0.1874308518500746,"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."}}