{"id":"W2550389299","doi":"10.2118/181018-ms","title":"Automatic Performance Analysis and Estimation of Risk Level Embedded in Drilling Operation Plans","year":2016,"lang":"en","type":"article","venue":"","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"ConocoPhillips (Canada)","funders":"Norges Forskningsråd","keywords":"Drilling; Context (archaeology); Computer science; Measurement while drilling; Probabilistic logic; Task (project management); Risk analysis (engineering); Reliability engineering; Engineering; Geology; Systems engineering; Mechanical engineering; Artificial intelligence","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":[0.0001387371,0.00006852514,0.0001323812,0.0002210894,0.00001420248,0.000009154067,0.0000297909,0.00003335568,0.00002284058],"category_scores_gemma":[0.00002515967,0.00005039632,0.00001913344,0.0002115821,0.000007633576,0.0001509469,0.000004938894,0.00003383929,0.000004505438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002193767,"about_ca_system_score_gemma":0.000002990093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002918154,"about_ca_topic_score_gemma":0.000070097,"domain_scores_codex":[0.9995807,0.000006513115,0.0001906111,0.00007193156,0.00006154407,0.00008873914],"domain_scores_gemma":[0.9997993,0.00006890357,0.00001776322,0.00008484237,0.00000860172,0.00002059352],"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":[6.109397e-7,0.000002222867,0.01353483,0.00003920795,0.00003488586,1.13457e-7,0.0002680308,0.9275739,0.001375526,0.00002338588,0.000001603615,0.05714566],"study_design_scores_gemma":[0.0001540223,0.000005195488,0.08458184,0.00005390552,0.00003737535,4.069434e-7,0.00001401042,0.9027455,0.01232192,0.00001494466,0.000001249531,0.000069651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.569366,0.0000149375,0.4303992,0.000002326542,0.00002066328,0.00002138993,0.00000430764,0.00006640721,0.0001047696],"genre_scores_gemma":[0.987878,0.0001278042,0.01195179,8.62523e-7,0.000007573216,0.000003419109,0.000004008766,0.000008370876,0.00001821616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.418512,"threshold_uncertainty_score":0.2055102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007357835788861226,"score_gpt":0.1931046602668654,"score_spread":0.1857468244780041,"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."}}