{"id":"W4386698740","doi":"10.3389/frai.2023.1243584","title":"XGSleeve: detecting sleeve incidents in well completion by using XGBoost classifier","year":2023,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alberta Innovates; Alberta Machine Intelligence Institute","keywords":"Process safety; Computer science; Artificial intelligence; Process (computing); Cluster analysis; Engineering; Operations management; Work in process","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005399692,0.0002096782,0.0003228161,0.0006910455,0.0000997735,0.0000700198,0.0002885719,0.0001720271,0.00005733938],"category_scores_gemma":[0.0001197417,0.000233131,0.00008257975,0.0016258,0.00007209173,0.0001810659,0.00006369458,0.000459206,0.0001933417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003928183,"about_ca_system_score_gemma":0.00001592618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006553868,"about_ca_topic_score_gemma":0.0006570582,"domain_scores_codex":[0.9981512,0.00008049833,0.0006045338,0.0003308289,0.0002853889,0.000547489],"domain_scores_gemma":[0.9995192,0.00007026899,0.00005438523,0.0002417461,0.00002796648,0.00008644789],"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":[0.0000156725,0.0000274016,0.008170529,0.00004000368,0.00002870599,0.00004225519,0.00109057,0.9456763,0.004440692,0.000005688952,0.002141342,0.03832087],"study_design_scores_gemma":[0.00003689086,0.000009388968,0.0006289193,0.00009223528,0.0000104305,0.000001646305,0.001551486,0.9691644,0.02571013,0.00112232,0.001402116,0.0002700427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6321716,0.0002558416,0.3651719,0.00005434117,0.001381647,0.0001457225,0.000006391215,0.0002221148,0.0005904119],"genre_scores_gemma":[0.9980518,0.0001387358,0.001499923,0.00002621021,0.00009625932,0.00001503056,0.00002139211,0.00004131407,0.0001094024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3658801,"threshold_uncertainty_score":0.9506807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03333878661380466,"score_gpt":0.2732833807004512,"score_spread":0.2399445940866466,"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."}}