{"id":"W2165836170","doi":"10.1109/tpwrd.2003.822533","title":"Power Quality Disturbance Classification Using the Inductive Inference Approach","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":171,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Decision tree; Artificial intelligence; Pattern recognition (psychology); Computer science; Inference; Machine learning; Tree (set theory); Decision tree learning; Feature (linguistics); Wavelet; Data mining; 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":[0.0002559123,0.000231598,0.0001946955,0.00009006433,0.0002859463,0.00006868292,0.0002634769,0.0001602766,0.00005282012],"category_scores_gemma":[0.000006742355,0.0001986319,0.000136421,0.0003534425,0.000152875,0.0003895689,0.000001727469,0.0006167809,0.00005527222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003481864,"about_ca_system_score_gemma":0.00007414301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009771098,"about_ca_topic_score_gemma":0.00001830846,"domain_scores_codex":[0.99869,0.00008527545,0.0003428668,0.0002907261,0.0002971953,0.0002939003],"domain_scores_gemma":[0.9991589,0.0001034994,0.00006032054,0.0005103425,0.00008609783,0.00008090529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001402121,0.0008919423,0.00004151452,0.00009037281,0.0003833615,0.000005441188,0.01411711,0.9414643,0.03118142,0.008845211,0.0001508487,0.00268828],"study_design_scores_gemma":[0.0158767,0.001575922,0.08379491,0.001340507,0.001401806,0.0002613477,0.05068462,0.3452396,0.4338257,0.02355117,0.02900857,0.01343911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3446631,0.0001042141,0.6521211,0.00008690191,0.0006604963,0.0001738704,0.00005083172,0.0001952143,0.001944358],"genre_scores_gemma":[0.9983521,0.00008220744,0.001255546,0.0001725916,0.00001952979,0.00003491915,0.000003542301,0.00003218041,0.00004741478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.653689,"threshold_uncertainty_score":0.8099973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07685166972690759,"score_gpt":0.2918944299530481,"score_spread":0.2150427602261405,"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."}}