{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005530012,0.0002294969,0.000414795,0.0003872157,0.0001612052,0.001351668,0.0003259754,0.0001143453,0.0001878442],"category_scores_gemma":[0.00007339447,0.0002034077,0.00008674188,0.0003869258,0.00001964685,0.001685748,0.0001253184,0.0002796879,0.000001475103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004864241,"about_ca_system_score_gemma":0.00003582163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005894077,"about_ca_topic_score_gemma":0.00005235,"domain_scores_codex":[0.9987321,0.00009510415,0.0004964659,0.0002657649,0.0002135573,0.0001969893],"domain_scores_gemma":[0.9988929,0.0004840858,0.0001173491,0.000162299,0.0002499251,0.00009341852],"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":[0.00004520203,0.00002509884,0.001549383,0.0008635296,0.0004629881,0.000009707367,0.0008991651,0.9510733,0.02046187,0.0004092111,0.02085244,0.003348061],"study_design_scores_gemma":[0.0002844026,0.000004778943,0.003525618,0.0004460224,0.0001546857,0.00001604062,0.00005380563,0.9688955,0.007351754,0.001014791,0.01798696,0.0002656704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09702965,0.174474,0.7153814,0.0003864769,0.005010972,0.003638614,0.0004014124,0.0009831354,0.002694394],"genre_scores_gemma":[0.9761769,0.001023801,0.02202884,0.00003207897,0.0002832777,0.00004452495,0.00003294919,0.00006350548,0.0003141101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8791473,"threshold_uncertainty_score":0.999685,"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."}}