{"id":"W2907198993","doi":"10.1109/iecon.2018.8591684","title":"Transient Event Classification Based on Wavelet Neuronal Network and Matched Filters","year":2018,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Smart grid; Context (archaeology); Transient (computer programming); Event (particle physics); Wavelet; Feature extraction; Energy (signal processing); Wavelet transform; Data mining; Artificial intelligence; Filter (signal processing); Process (computing); Building automation; Energy consumption; Pattern recognition (psychology); Real-time computing; Engineering; Computer vision; Mathematics","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":[0.00008665445,0.0001025481,0.00007297896,0.00004153837,0.00003680213,0.00001952484,0.00005968593,0.00002837455,0.000202816],"category_scores_gemma":[0.000002492029,0.00009610908,0.00002260051,0.00009255402,0.00002567723,0.00003208693,0.000008740241,0.00004913758,0.00004703008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003717936,"about_ca_system_score_gemma":0.00000320294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003835642,"about_ca_topic_score_gemma":0.00001398699,"domain_scores_codex":[0.999414,0.00001479747,0.0001179684,0.0001501085,0.0001245963,0.0001785537],"domain_scores_gemma":[0.9997196,0.00002408814,0.000009816073,0.0001818154,0.00001082877,0.00005384799],"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":[0.00002827315,0.00003037731,0.0004118257,0.00002901681,0.00002931139,0.000001974,0.0000669174,0.9207557,0.001150141,0.002223893,0.06770945,0.007563153],"study_design_scores_gemma":[0.0002285991,0.00007039487,0.0999048,0.00001089386,0.000008083181,3.146664e-7,0.00001076344,0.8682275,0.0007672837,0.00002221244,0.03064321,0.0001059287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5461347,0.00001688289,0.3900326,0.001980119,0.001858275,0.0003162514,0.000003724982,0.0007055655,0.05895191],"genre_scores_gemma":[0.996756,0.000006920392,0.002195085,0.0005267896,0.0002720773,0.00002009052,0.00001146791,0.00002150044,0.0001901192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4506213,"threshold_uncertainty_score":0.3919215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01311260051441772,"score_gpt":0.2014507847402869,"score_spread":0.1883381842258692,"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."}}