{"id":"W2489794998","doi":"10.2118/03-01-05","title":"Numerical Study and Economic Evaluation of SAGD Wind-Down Methods","year":2003,"lang":"en","type":"article","venue":"Journal of Canadian Petroleum Technology","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Taiwan University","keywords":"Steam-assisted gravity drainage; Steam injection; Petroleum engineering; Environmental science; Process (computing); Fossil fuel; Production (economics); Enhanced oil recovery; Waste management; Engineering; Oil sands; Asphalt; Materials science; Computer science","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":[0.002394547,0.0008078938,0.0008615362,0.001144288,0.0008182349,0.001236294,0.001361579,0.001766533,0.005391439],"category_scores_gemma":[0.007548745,0.0004213391,0.0006118693,0.0007770224,0.001120926,0.0009263702,0.00139,0.001127015,0.0003636882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001304697,"about_ca_system_score_gemma":0.001092871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009005032,"about_ca_topic_score_gemma":0.005549204,"domain_scores_codex":[0.9993831,0.0003001314,0.0000323128,0.00004366689,0.0001705812,0.00007025163],"domain_scores_gemma":[0.9942239,0.003998143,0.000342135,0.0002506171,0.0009986748,0.0001865429],"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":[0.0001609466,0.00011252,0.001267581,0.0001200835,0.00002052474,0.00009519704,0.00002912919,0.9786224,0.001005462,0.006304412,0.000501067,0.01176069],"study_design_scores_gemma":[0.000009021543,0.00003765815,0.00006349855,0.000007421555,0.000002700574,0.000005424043,0.00001010088,0.9993175,0.0001217682,0.0002702617,0.0001522451,0.000002500824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3792225,0.003543809,0.5667549,0.002368656,0.0007485964,0.0007983424,0.0005973672,0.0005858723,0.04537997],"genre_scores_gemma":[0.86805,0.0005799245,0.1272784,0.000116387,0.00008076112,0.0002835976,0.0001913273,0.00008796703,0.003331676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009005032,"threshold_uncertainty_score":0.01803619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02259521329451604,"score_gpt":0.3120469803341985,"score_spread":0.2894517670396825,"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."}}