{"id":"W2083333579","doi":"10.1016/j.eswa.2009.07.022","title":"On simulation and optimization of one natural gas industry system under the rough environment","year":2009,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Science Fund for Distinguished Young Scholars; Ministry of Education, India; Ministry of Earth Sciences","keywords":"Computer science; Natural gas; Natural gas industry; Natural (archaeology); Rough set; Artificial intelligence; Industrial engineering; Chemistry; Geology","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":[0.0006469431,0.0005434899,0.001451758,0.0006132948,0.0008101822,0.0008581063,0.0006751019,0.001773321,0.002271966],"category_scores_gemma":[0.002251544,0.0004201186,0.0009391641,0.0004970879,0.001028876,0.0007791188,0.0007972236,0.000890404,0.000098815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000738633,"about_ca_system_score_gemma":0.0009865265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04188563,"about_ca_topic_score_gemma":0.01658129,"domain_scores_codex":[0.9997454,0.0001043423,0.00001001271,0.00003798884,0.00003927261,0.00006286365],"domain_scores_gemma":[0.9985846,0.001095187,0.00008248404,0.00004639519,0.0001175774,0.00007373233],"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":[0.00001304907,0.00000886316,0.0001717268,0.000005840864,0.00000574667,0.0000173846,0.000007909414,0.9987522,0.00006544949,0.0005234918,0.00003095996,0.0003973769],"study_design_scores_gemma":[0.000002138019,0.000005748346,0.00005483418,3.880724e-7,0.000001819413,0.000001316804,0.000004281936,0.9997498,0.00002803205,0.0001322435,0.00001797578,0.000001424764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7975311,0.0006232202,0.1804621,0.001068041,0.0001409156,0.00008706853,0.0002877344,0.0003409202,0.01945897],"genre_scores_gemma":[0.9925727,0.0001192267,0.005699924,0.00003154456,0.00001227924,0.00002860447,0.00005614226,0.00001610707,0.001463474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04188563,"threshold_uncertainty_score":0.0832836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01612732450057241,"score_gpt":0.256695246263357,"score_spread":0.2405679217627846,"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."}}