{"id":"W2402355997","doi":"","title":"Efficient Incremental Smart Grid Data Analytics","year":2016,"lang":"en","type":"article","venue":"EDBT/ICDT Workshops","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Smart meter; Smart grid; Computer science; Context (archaeology); Computation; Electricity; Scalability; Popularity; Big data; Grid; Distributed computing; Energy consumption; Efficient energy use; Database; Real-time computing; Data mining; Algorithm; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009329328,0.001264123,0.001199996,0.001039965,0.0006833936,0.002047417,0.003500838,0.0006041074,0.005540172],"category_scores_gemma":[0.005568055,0.0007074067,0.0008032779,0.002584003,0.0006607663,0.003747439,0.002712949,0.001470706,0.002436587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008558542,"about_ca_system_score_gemma":0.002021511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01041414,"about_ca_topic_score_gemma":0.01271169,"domain_scores_codex":[0.9985795,0.0001960084,0.00007424351,0.0002848141,0.0006559159,0.0002094754],"domain_scores_gemma":[0.9966716,0.001241162,0.0001368864,0.001265687,0.0005005462,0.0001840976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00135192,0.0005669165,0.0076182,0.0004003474,0.0001713729,0.0003865548,0.0005689319,0.2243709,0.02816685,0.01812962,0.0847603,0.6335081],"study_design_scores_gemma":[0.00005180688,0.00002912984,0.0007257622,0.000004868089,0.00001211775,0.00004173314,0.0000763682,0.981918,0.004766958,0.008491176,0.003870674,0.00001143065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1031447,0.0006770997,0.8349469,0.001427821,0.00036665,0.0003874642,0.003414467,0.0459916,0.009643367],"genre_scores_gemma":[0.5171967,0.0002980493,0.469386,0.0002636077,0.0001612131,0.0003453412,0.00733417,0.0009322586,0.004082563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01041414,"threshold_uncertainty_score":0.02070707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03474347714948078,"score_gpt":0.254617798898238,"score_spread":0.2198743217487572,"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."}}