{"id":"W2039637288","doi":"10.1115/ipc2010-31016","title":"Neural Network Based Predictive Emission Monitoring Module for a GE LM2500 Gas Turbine","year":2010,"lang":"en","type":"article","venue":"","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"NOx; Perceptron; Range (aeronautics); Artificial neural network; Gas compressor; Environmental science; Turbine; Standard deviation; Computer science; Engineering; Statistics; Mechanical engineering; Mathematics; Chemistry; Artificial intelligence; Aerospace engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002128147,0.0007289478,0.0003551921,0.0002489362,0.0002995872,0.0003691211,0.0008721369,0.0004529897,0.002018267],"category_scores_gemma":[0.0003690413,0.0002803438,0.000335563,0.0002298518,0.000240417,0.0003475921,0.0002623969,0.000531024,0.0003221185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001331524,"about_ca_system_score_gemma":0.001004731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08644789,"about_ca_topic_score_gemma":0.06869277,"domain_scores_codex":[0.9999185,0.00001127652,0.000003284677,0.00002687735,0.00002993233,0.00001009714],"domain_scores_gemma":[0.9999242,0.00002898959,0.000007772113,0.000005633175,0.00002948427,0.000004048842],"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":[0.00009739485,0.00005175937,0.003557222,0.00003174538,0.00002075597,0.00007198402,0.00002022849,0.9770214,0.003766803,0.0002154517,0.0004130129,0.01473228],"study_design_scores_gemma":[0.000005024568,0.0000232015,0.001267655,0.000001813544,0.000005478797,0.000006500848,0.000004888665,0.9967237,0.001685529,0.00007589111,0.0001953013,0.000004964306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8289021,0.0002113795,0.1543155,0.0002924219,0.00005725997,0.000173876,0.001268337,0.003058975,0.01172023],"genre_scores_gemma":[0.9860201,0.0000482637,0.01026143,0.00002052025,0.000003193398,0.0000583993,0.0004320387,0.00002356071,0.003132525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08644789,"threshold_uncertainty_score":0.1718894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009981000069974339,"score_gpt":0.2265298775850774,"score_spread":0.216548877515103,"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."}}