{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003057995,0.0001171255,0.0002581602,0.0004950682,0.00002644426,0.0000114814,0.0001971739,0.0001615384,0.00004237631],"category_scores_gemma":[0.00003688285,0.00008533307,0.00006538277,0.0001982205,0.00006194736,0.0001840797,0.00001831988,0.0001558359,0.000006757126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001267284,"about_ca_system_score_gemma":0.00003519523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007886348,"about_ca_topic_score_gemma":0.000007704667,"domain_scores_codex":[0.9987277,0.00001733469,0.000769044,0.0001086942,0.0002025235,0.0001746961],"domain_scores_gemma":[0.9990792,0.00001552775,0.000453521,0.000207857,0.0001961885,0.00004772477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008479617,0.00002715945,0.0008910117,0.00002905158,0.00005828591,7.1123e-7,0.00001604624,0.00002634378,0.8357108,0.001341473,0.01363756,0.1482531],"study_design_scores_gemma":[0.0004459063,0.0005301976,0.0004360118,0.00004778336,0.00003254677,0.0000278577,0.001099495,0.0001208828,0.9580356,0.001296567,0.03780028,0.0001268192],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9418172,0.0007219568,0.04679066,0.00939353,0.0007454641,0.0001281558,0.00003097989,0.000300986,0.00007105678],"genre_scores_gemma":[0.9985294,0.0003466079,0.0008798155,0.00001078601,0.0001411818,0.00001794416,0.000001761367,0.00001950452,0.00005295176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1481263,"threshold_uncertainty_score":0.3479782,"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."}}