{"id":"W2144876719","doi":"10.1109/tpwrd.2002.807462","title":"An approach to implement electricity metering in real-time using artificial neural networks","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Metering mode; Electricity; Artificial neural network; Electric power system; Computer science; Electric power; Electricity market; Process (computing); Automotive engineering; Real-time computing; Engineering; Power (physics); Artificial intelligence; Electrical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002323319,0.0002421072,0.0002254716,0.0003180987,0.000130447,0.00005869726,0.0001213553,0.000103322,0.00007525962],"category_scores_gemma":[0.000001790865,0.0002740543,0.00009024631,0.0005669995,0.00001004915,0.0002298501,8.452554e-7,0.0002999167,0.000008235982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001960001,"about_ca_system_score_gemma":0.00001651532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001578222,"about_ca_topic_score_gemma":0.00006261121,"domain_scores_codex":[0.9985793,0.00008612376,0.0003450736,0.0002978787,0.0001593827,0.0005322582],"domain_scores_gemma":[0.9995055,0.00004603303,0.0000230254,0.0002454153,0.00002506473,0.0001549863],"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":[0.00002514467,0.0001054995,0.00001174686,0.000005951615,0.00002406053,0.000006512551,0.0002074602,0.9571954,0.03905597,0.00005925058,0.000008773901,0.003294219],"study_design_scores_gemma":[0.0001766235,0.0001075598,0.00004786123,0.00001783611,0.00002115115,0.00001905998,0.00005712737,0.9701049,0.02904224,0.00001251534,0.00006667704,0.0003264438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5193604,0.00001461093,0.4786143,0.000001216269,0.0003825722,0.0001058522,0.000006518843,0.0001504442,0.001364033],"genre_scores_gemma":[0.9950334,0.00001561265,0.004776869,0.00004265522,0.00003647453,0.00002339333,0.000003689419,0.00005664964,0.00001120363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.475673,"threshold_uncertainty_score":0.9999712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02579802336824112,"score_gpt":0.2405537396028969,"score_spread":0.2147557162346558,"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."}}