{"id":"W3007849172","doi":"10.1287/ijoc.2019.0904","title":"Transient-State Natural Gas Transmission in Gunbarrel Pipeline Networks","year":2020,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Compressor station; Mathematical optimization; Computer science; Natural gas; Benchmark (surveying); Gas compressor; Energy consumption; Pipeline (software); Heuristic; Dynamic programming; Markov decision process; Curse of dimensionality; Upper and lower bounds; Mathematics; Engineering; Markov process; 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":[0.0005265308,0.0005915054,0.0005171179,0.0003410495,0.0003977157,0.00063623,0.0008543073,0.0009067082,0.001584682],"category_scores_gemma":[0.001799661,0.0004036512,0.0004712479,0.0005635459,0.0006675177,0.001222874,0.0006172445,0.0006336322,0.00009757266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001896283,"about_ca_system_score_gemma":0.0007896838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01812516,"about_ca_topic_score_gemma":0.01444146,"domain_scores_codex":[0.9997866,0.00007514652,0.000005819898,0.00005234249,0.00002818166,0.00005204215],"domain_scores_gemma":[0.9993021,0.0004777318,0.00009131868,0.00002016266,0.000070938,0.00003773324],"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":[0.00002446189,0.000008776433,0.0003166062,0.00001641462,0.000005678609,0.00004432886,0.00001137003,0.9949828,0.0003204134,0.002140068,0.0001504191,0.001978593],"study_design_scores_gemma":[0.000003751619,0.00001344229,0.0001345587,0.000002432339,0.000002706257,0.000007487523,0.00002126329,0.9979531,0.000175126,0.001551522,0.000132368,0.000002155504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5972387,0.0008656256,0.3910414,0.0009782609,0.00005686816,0.00008933582,0.0005455489,0.0002573356,0.008926956],"genre_scores_gemma":[0.9824703,0.0002332231,0.01517525,0.00005009432,0.000006886645,0.00004354358,0.0001750257,0.00002254952,0.001823051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01812516,"threshold_uncertainty_score":0.03603935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008294573894365017,"score_gpt":0.2002901184201685,"score_spread":0.1919955445258035,"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."}}