{"id":"W3037903850","doi":"10.1609/aaai.v34i08.7027","title":"PIDS: An Intelligent Electric Power Management Platform","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Science Council; National Natural Science Foundation of China","keywords":"Electricity; Electric power system; Computer science; Consumption (sociology); Process (computing); Power consumption; Power management; Power (physics); Electric power; Demand response; Business; Environmental economics; Operations research; Operations management; Telecommunications; Risk analysis (engineering); Engineering; Economics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002140614,0.0002650605,0.0002371775,0.000101538,0.0001440035,0.0001153965,0.0008726474,0.00008793033,0.0002023415],"category_scores_gemma":[0.00007735621,0.0002117528,0.0001034367,0.0006238139,0.00008311974,0.0002680453,0.0001176766,0.0004042299,0.0001232933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004602702,"about_ca_system_score_gemma":0.00001518201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005996859,"about_ca_topic_score_gemma":0.000003299005,"domain_scores_codex":[0.9984364,0.00000606542,0.0004941954,0.0003237283,0.0003507252,0.0003888702],"domain_scores_gemma":[0.9993706,0.00002969856,0.0001139426,0.0001583142,0.0001531236,0.0001742621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001475799,0.0001423312,0.0001509438,0.0002427311,0.000101667,0.000002739936,0.004863363,0.009759173,0.07917509,0.7500253,0.0006188406,0.1547703],"study_design_scores_gemma":[0.00002765797,0.0003540101,0.00006067479,0.0002008371,0.00002924494,0.00000327943,0.001791927,0.1613237,0.8059888,0.02839777,0.001437261,0.0003847679],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8387117,0.0001024798,0.005232191,0.0008724029,0.0007183659,0.0005326549,0.000007765064,0.0005251395,0.1532972],"genre_scores_gemma":[0.9988927,0.0001298479,0.0005323034,0.000221815,0.0001030304,0.00001883749,0.000001131179,0.00003568398,0.00006463278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7268137,"threshold_uncertainty_score":0.863503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06675763103910648,"score_gpt":0.2556870596545758,"score_spread":0.1889294286154693,"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."}}