{"id":"W6937026322","doi":"10.6047/j.issn.1000-8241.2024.06.013","title":"Self-adaptive simulation method for natural gas pipeline considering temperature and pressure compensations","year":2024,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"","keywords":"SCADA; Pipeline (software); Natural gas; Pipeline transport; Process (computing); Volume (thermodynamics); Control theory (sociology); Flow (mathematics); Approximation error; Computer simulation","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":[0.0002849154,0.0004691229,0.0004443329,0.0003920494,0.000400453,0.0005404457,0.0007515223,0.0005997707,0.002286452],"category_scores_gemma":[0.0006982393,0.0002554715,0.0007133,0.0002521115,0.0003586988,0.000603198,0.0005962106,0.0004825258,0.0002775881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004143384,"about_ca_system_score_gemma":0.0008757393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007577764,"about_ca_topic_score_gemma":0.003996196,"domain_scores_codex":[0.9998379,0.00003298212,0.00001031446,0.00003974255,0.00006448687,0.00001448685],"domain_scores_gemma":[0.9998147,0.00005788209,0.00002536952,0.00001845932,0.00007112121,0.00001246317],"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":[0.00003593475,0.00002020097,0.0005894187,0.0000399463,0.00001477806,0.0000691667,0.00006946596,0.9686114,0.006064447,0.003927173,0.0003035576,0.02025448],"study_design_scores_gemma":[0.000001625475,0.000004598228,0.00002392959,7.934225e-7,0.000001109201,0.000004093658,0.000001834323,0.9993435,0.0002483297,0.0001574694,0.000211616,0.000001132528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01746712,0.00008090684,0.9790283,0.00006514347,0.0000445223,0.00003309469,0.00002454571,0.0005720418,0.002684391],"genre_scores_gemma":[0.8362188,0.0002361592,0.1573386,0.0000581977,0.00003752577,0.0002350083,0.0001382718,0.0001167839,0.005620784],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007577764,"threshold_uncertainty_score":0.01506734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1228585662098507,"score_gpt":0.4828119994374724,"score_spread":0.3599534332276218,"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."}}