{"id":"W2388796209","doi":"10.1109/tpds.2015.2470238","title":"A Distributed and Scalable Approach to Semi-Intrusive Load Monitoring","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Science and Technology Commission of Shanghai Municipality; Natural Sciences and Engineering Research Council of Canada; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Computer science; Scalability; Metering mode; Distributed computing; Energy consumption; Energy (signal processing); Real-time computing; TRACE (psycholinguistics); Load management; Constraint (computer-aided design); Scale (ratio); Power (physics); Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001870468,0.0002874074,0.0003247776,0.0001028105,0.0001390008,0.0001567222,0.0001159155,0.0001225279,0.0000020492],"category_scores_gemma":[0.000007561914,0.0002821434,0.00004294439,0.0003667452,0.00003668173,0.0001582984,0.000004686552,0.0001806675,0.00002976383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003039414,"about_ca_system_score_gemma":0.0000234736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002385947,"about_ca_topic_score_gemma":0.000009056943,"domain_scores_codex":[0.9986019,0.00004181816,0.0003029467,0.0003509272,0.0003145641,0.0003878899],"domain_scores_gemma":[0.9990898,0.00003814932,0.0000292826,0.0002854599,0.00009584191,0.0004614388],"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":[0.0000419414,0.00006116054,0.0001436222,0.00009936745,0.0001170307,0.000006343286,0.0001912182,0.9950584,0.0001265844,0.0000707566,0.003819125,0.0002644367],"study_design_scores_gemma":[0.002771754,0.0002122271,0.00128245,0.0002191689,0.0001729992,0.00009936415,0.003325511,0.9606867,0.0008338073,0.00004450414,0.02932956,0.001021999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0470731,0.0005437835,0.9486354,0.00004779981,0.001216896,0.0004099929,0.0004391244,0.0004092219,0.001224692],"genre_scores_gemma":[0.9987065,0.00008649469,0.0004594917,0.000009780558,0.0001234735,0.0002966201,0.00005805421,0.0000336682,0.0002259856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9516333,"threshold_uncertainty_score":0.999963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02291188623907258,"score_gpt":0.2162987830223589,"score_spread":0.1933868967832863,"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."}}