{"id":"W4403862246","doi":"10.2139/ssrn.4995590","title":"Active Control of Natural Gas Pipeline System Based on Box-Jenkins Method","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Box–Jenkins; Pipeline (software); Natural gas; Control (management); Computer science; Natural (archaeology); Petroleum engineering; Engineering; Artificial intelligence; Machine learning; Waste management; Geology; Operating system; Time series","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007047751,0.0003698606,0.0005399677,0.0002513829,0.00007619207,0.00004257272,0.000359759,0.0002116829,0.000008374835],"category_scores_gemma":[0.00002343523,0.000322771,0.0003478471,0.0001839967,0.00002375406,0.00003708716,0.00006490952,0.006685427,0.00001941808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002094816,"about_ca_system_score_gemma":0.001003679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000239477,"about_ca_topic_score_gemma":0.00005830351,"domain_scores_codex":[0.9975641,0.00006881241,0.0005060305,0.0003115996,0.0003159853,0.001233416],"domain_scores_gemma":[0.9990872,0.0001404025,0.0002046199,0.0003476333,0.0001373013,0.00008281669],"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":[0.0000850428,0.00005570418,0.000001677021,0.0004266702,0.000682659,0.00001092109,0.00006900509,0.7869766,0.001742075,0.08887441,0.0001120915,0.1209631],"study_design_scores_gemma":[0.0007051385,0.00008611651,0.000006403945,0.0005073612,0.0002380486,0.0001246973,0.0003795493,0.9152703,0.002675597,0.07876707,0.0009015509,0.0003382175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001428414,0.005728895,0.9894173,0.0003601652,0.0009403145,0.0004055957,0.0001698396,0.0002601047,0.001289438],"genre_scores_gemma":[0.992133,0.0007170583,0.005931425,0.00002426687,0.0007682558,0.00006964347,0.00003080859,0.0001027153,0.0002228857],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9907045,"threshold_uncertainty_score":0.9999225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004212592488672149,"score_gpt":0.2517034195499506,"score_spread":0.2474908270612785,"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."}}