{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005727341,0.0008671515,0.0007390058,0.0002923158,0.0003593288,0.001020386,0.0009345854,0.000847674,0.001733733],"category_scores_gemma":[0.001203653,0.0004261723,0.0007556123,0.0002873112,0.0005468537,0.001214462,0.0006607994,0.001383685,0.000237774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007499037,"about_ca_system_score_gemma":0.001037688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01518807,"about_ca_topic_score_gemma":0.006922553,"domain_scores_codex":[0.999696,0.00005416835,0.00001547951,0.000122217,0.00007832079,0.00003378863],"domain_scores_gemma":[0.9996104,0.0001756885,0.00004087809,0.00002455572,0.0001319168,0.00001662376],"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":[0.0000293817,0.0000179331,0.0006172405,0.00006416807,0.00002723395,0.00006375554,0.00003963749,0.9819455,0.002069154,0.004200969,0.000410927,0.01051418],"study_design_scores_gemma":[0.00000105106,0.00000504951,0.00006245052,0.000001482615,0.000001885835,0.000002841545,0.00000212486,0.9992672,0.0001597954,0.0004131156,0.00008133642,0.000001751212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04368818,0.0006911645,0.9486623,0.0004950547,0.0001139195,0.00005028312,0.0001743389,0.0004620223,0.005662679],"genre_scores_gemma":[0.9759291,0.0004581332,0.01750621,0.0001107279,0.00003641431,0.0001282069,0.0002399516,0.00003521357,0.005556204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01518807,"threshold_uncertainty_score":0.03019935,"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."}}