{"id":"W1510952876","doi":"10.1088/0957-0233/26/6/065604","title":"A framework with nonlinear system model and nonparametric noise for gas turbine degradation state estimation","year":2015,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Life Prediction Technologies (Canada); Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonparametric statistics; Nonlinear system; Degradation (telecommunications); Gas turbines; Noise (video); Estimation; State (computer science); Turbine; Computer science; Environmental science; Control theory (sociology); Mathematics; Econometrics; Algorithm; Physics; Artificial intelligence; Engineering; Thermodynamics; Telecommunications; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001712211,0.00139097,0.001263136,0.0007081058,0.000408596,0.0009507957,0.001577527,0.001287343,0.001350246],"category_scores_gemma":[0.005045976,0.0007095058,0.001232491,0.0007295899,0.0009498286,0.001201219,0.001263712,0.00198752,0.0004735235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008029879,"about_ca_system_score_gemma":0.001448072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01357649,"about_ca_topic_score_gemma":0.007585295,"domain_scores_codex":[0.9991481,0.000254713,0.00004491768,0.000268628,0.0001967905,0.0000868985],"domain_scores_gemma":[0.9984987,0.0008742549,0.0002103581,0.0001052673,0.0002689552,0.00004241409],"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":[0.00004240807,0.00002421331,0.000633584,0.00007394004,0.00005369468,0.0000921886,0.00004917359,0.9598752,0.001048915,0.01966367,0.0005471358,0.01789588],"study_design_scores_gemma":[0.000002384379,0.000009620429,0.00007223363,0.000002253834,0.000005929625,0.000007991879,0.000002149472,0.9975194,0.00009687735,0.002024794,0.0002513529,0.00000483722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001897584,0.0001431722,0.9973521,0.00006159397,0.00002519172,0.000009412452,0.00003866735,0.0001082679,0.0003640552],"genre_scores_gemma":[0.7181281,0.001633766,0.2689475,0.0002408167,0.0004083889,0.000529178,0.0008454743,0.0001740544,0.00909274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01357649,"threshold_uncertainty_score":0.02699488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02258184954430753,"score_gpt":0.2276975479817538,"score_spread":0.2051156984374462,"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."}}