{"id":"W2024866674","doi":"10.1115/icone18-29777","title":"Fault Detection and Identification in NPP Instruments Using Kernel Principal Component Analysis","year":2010,"lang":"en","type":"article","venue":"18th International Conference on Nuclear Engineering: Volume 1","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Principal component analysis; Kernel principal component analysis; Fault detection and isolation; Identification (biology); Kernel (algebra); Computer science; Fault (geology); Pattern recognition (psychology); Artificial intelligence; Component (thermodynamics); Feature extraction; Isolation (microbiology); Support vector machine; Data mining; Kernel method; Mathematics; Geology","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":[0.000150483,0.0001757795,0.0001910581,0.0005487718,0.00003593121,0.0001644115,0.00017556,0.0001191873,0.0001364383],"category_scores_gemma":[0.00003354064,0.000204743,0.00006551026,0.0002756286,0.0000203832,0.0001983288,0.00002918844,0.0003614193,0.0000440341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000140168,"about_ca_system_score_gemma":0.000007553458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001335337,"about_ca_topic_score_gemma":0.0001525977,"domain_scores_codex":[0.9989414,0.00001398885,0.0003458718,0.000250366,0.0002658723,0.0001825194],"domain_scores_gemma":[0.9995978,0.000009593498,0.00005946943,0.0001765777,0.00006662992,0.00008997002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005666808,0.00007930686,0.00562109,0.00005065124,0.0005096426,0.000007705084,0.0003905936,0.2519493,0.725248,0.0069255,0.00001436765,0.009147073],"study_design_scores_gemma":[0.0003507316,0.00001635651,0.0496201,0.00001725322,0.00002715767,0.0000101479,0.00005740883,0.9457471,0.0006957911,0.00001365813,0.003261243,0.0001829945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940474,0.00000500016,0.003268406,0.00004575328,0.001442639,0.000140984,0.00001150774,0.0003205854,0.0007177262],"genre_scores_gemma":[0.999517,0.00001079506,0.0002177181,0.000012284,0.00008914305,0.0000169797,0.000007830033,0.00004133072,0.00008690386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7245523,"threshold_uncertainty_score":0.8349176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01466346353023667,"score_gpt":0.2356241836662697,"score_spread":0.2209607201360331,"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."}}