{"id":"W2583578062","doi":"10.2118/0716-0070-jpt","title":"Run-Life Improvement by Implementation of Artificial-Lift-Systems Failure Classification","year":2016,"lang":"en","type":"article","venue":"Journal of Petroleum Technology","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial lift; Casing; Lift (data mining); Artificial intelligence; Engineering; Computer science; Forensic engineering; Mechanical engineering; Machine learning; Petroleum engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004609292,0.001573529,0.001096124,0.003877109,0.0006783886,0.002107734,0.001496717,0.0006377803,0.00240339],"category_scores_gemma":[0.01256807,0.0002893467,0.001253888,0.001255982,0.0005335985,0.001341202,0.001497042,0.001173977,0.001120237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001528308,"about_ca_system_score_gemma":0.001718668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005129576,"about_ca_topic_score_gemma":0.005109529,"domain_scores_codex":[0.996394,0.0008340245,0.0003418263,0.0005675148,0.001595358,0.0002673541],"domain_scores_gemma":[0.9878897,0.003076742,0.001879991,0.0010226,0.005757423,0.0003735527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006331354,0.0004728711,0.05544586,0.0006009298,0.0002202914,0.0002098249,0.0005638902,0.2235508,0.01308912,0.004098503,0.01230249,0.6888123],"study_design_scores_gemma":[0.00004751362,0.0006533618,0.02636065,0.0001562304,0.0001037446,0.0001273951,0.0003005429,0.9376162,0.01558881,0.004979646,0.01394761,0.0001182388],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.129567,0.0007579844,0.8477201,0.0008127729,0.0003972039,0.0007277473,0.001133407,0.01085858,0.008025213],"genre_scores_gemma":[0.5999926,0.0002489643,0.3929186,0.0001268095,0.0001539904,0.0003858685,0.00231238,0.000477159,0.003383614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005129576,"threshold_uncertainty_score":0.02437651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008525157851820607,"score_gpt":0.2419957480836774,"score_spread":0.2334705902318568,"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."}}