{"id":"W2069227812","doi":"10.2118/165482-ms","title":"Achieving Production Optimization Using Progressive Cavity Pumps, Artificial Neural Networks, and System-Based Monitoring","year":2013,"lang":"en","type":"article","venue":"SPE Heavy Oil Conference-Canada","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"","keywords":"Workflow; Software; Computer science; Artificial neural network; Production (economics); Identification (biology); Systems engineering; Real-time computing; Engineering; Database; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008330696,0.0008316807,0.000386135,0.0005173672,0.0002701655,0.0008390205,0.0004056956,0.0003753368,0.0006491792],"category_scores_gemma":[0.001357988,0.000244292,0.0002954387,0.0003584214,0.0002587272,0.0004932348,0.0004966055,0.0003821152,0.0001191383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008728377,"about_ca_system_score_gemma":0.0009239704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01065217,"about_ca_topic_score_gemma":0.008321135,"domain_scores_codex":[0.9995967,0.0000921954,0.00003145106,0.000114715,0.0001092831,0.00005574485],"domain_scores_gemma":[0.9993966,0.0002510075,0.000123638,0.0000513411,0.0001567196,0.00002073081],"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":[0.0001747424,0.0001578566,0.005393363,0.00007667792,0.00003944313,0.0000674646,0.0000490047,0.9077213,0.01902509,0.0005418675,0.0002850928,0.06646812],"study_design_scores_gemma":[0.000003940444,0.00006421068,0.001191754,0.000003996874,0.000006572634,0.000004751523,0.00001165831,0.9935289,0.004862772,0.0001711696,0.0001457991,0.000004482977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4906562,0.0001987799,0.5018393,0.0001831038,0.00002585468,0.000224481,0.0001767045,0.001726625,0.004968836],"genre_scores_gemma":[0.9676237,0.00003067747,0.03164451,0.00001227476,0.000003181493,0.00005217846,0.00005705174,0.0000136522,0.0005628128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01065217,"threshold_uncertainty_score":0.02118033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01751491785332208,"score_gpt":0.2067364697696036,"score_spread":0.1892215519162815,"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."}}