{"id":"W4221103822","doi":"10.1002/eng2.12495","title":"Research on data‐driven model for soft sensing of natural gas production system","year":2022,"lang":"en","type":"article","venue":"Engineering Reports","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"China University of Petroleum, Beijing; National Natural Science Foundation of China","keywords":"Nonlinear autoregressive exogenous model; Autoregressive model; Artificial neural network; Computer science; Black box; System identification; Engineering; Artificial intelligence; Data mining; Mathematics; Statistics; Measure (data warehouse)","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":[],"consensus_categories":[],"category_scores_codex":[0.001125786,0.0001165739,0.0001689368,0.0002821051,0.0001229355,0.00001380935,0.0001312099,0.00003387513,0.000001631612],"category_scores_gemma":[0.0001410744,0.0001295809,0.00004059022,0.0002832753,0.00001347096,0.0001202615,0.0001156422,0.0003715978,4.483156e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002092707,"about_ca_system_score_gemma":0.00002836992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008460607,"about_ca_topic_score_gemma":7.859147e-7,"domain_scores_codex":[0.9986979,0.00001315863,0.000319765,0.000348136,0.0003775156,0.0002434868],"domain_scores_gemma":[0.9990153,0.00003908551,0.00005154418,0.0007533945,0.0001068802,0.0000338408],"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":[0.000007669615,0.00001061848,0.000005343834,0.0003588619,0.00002248997,0.00001986536,0.0001135119,0.9762327,0.01449608,0.00007583408,0.004677144,0.003979848],"study_design_scores_gemma":[0.00003452114,0.00003349189,0.0000122235,0.0000648437,0.00000817063,0.0002679306,0.00008955569,0.9362543,0.06115096,0.00005755973,0.001905316,0.0001211597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8973083,0.0008203242,0.08787382,0.0002792431,0.007870214,0.001510444,0.00006884917,0.003883424,0.0003854024],"genre_scores_gemma":[0.9897915,0.000005507499,0.009541751,0.000001528924,0.0002632347,0.00005955944,0.00006422983,0.00005643476,0.0002163255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09248315,"threshold_uncertainty_score":0.5284154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04652872775454024,"score_gpt":0.2943574282635059,"score_spread":0.2478287005089656,"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."}}