{"id":"W7045369771","doi":"","title":"AIRCRAFT JET ENGINE CONDITION MONITORING
\\nTHROUGH SYSTEM IDENTIFICATION BY USING
\\nGENETIC PROGRAMMING","year":2013,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Concordia University","keywords":"Jet engine; Fault detection and isolation; Fault (geology); Condition monitoring; Identification (biology); Artificial neural network; Search engine; Sensitivity (control systems)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004836812,0.0005759546,0.0005557612,0.0004282601,0.0002407516,0.000841238,0.0005754316,0.0005141915,0.001252939],"category_scores_gemma":[0.001078983,0.0002331892,0.0006200924,0.0003113671,0.0003948375,0.0004596662,0.0005254714,0.0008119449,0.0002313699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004798501,"about_ca_system_score_gemma":0.0007211238,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003427437,"about_ca_topic_score_gemma":0.003149,"domain_scores_codex":[0.9996305,0.0001106271,0.00001905257,0.000109339,0.00009859943,0.00003195936],"domain_scores_gemma":[0.999713,0.0001663617,0.0000391111,0.00002396699,0.00005031361,0.000007237697],"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":[0.00005543271,0.00007466916,0.001524299,0.00008986099,0.00006638401,0.00007734212,0.0000888689,0.7981657,0.01038304,0.01189919,0.0005728939,0.1770024],"study_design_scores_gemma":[0.000003026078,0.00002583111,0.0002114662,0.000005269754,0.000007411613,0.00001456317,0.000006836916,0.9953412,0.001630435,0.002171255,0.0005783742,0.000004358517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008047174,0.00006943152,0.9904237,0.00006749974,0.00001149358,0.0000274444,0.00002008477,0.0002180049,0.001115127],"genre_scores_gemma":[0.4150884,0.0003957285,0.5783675,0.0001382717,0.00005145965,0.0002739002,0.000163576,0.00008471173,0.005436475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9965726,"threshold_uncertainty_score":0.006815016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01868246983062482,"score_gpt":0.2825524810824513,"score_spread":0.2638700112518265,"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."}}