{"id":"W2165263560","doi":"10.1109/icdcs.2007.83","title":"A Weighted Moving Average-based Approach for Cleaning Sensor Data","year":2007,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Moving average; Wireless sensor network; Data mining; Data quality; Real-time computing; Engineering; Computer vision","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":[0.002196502,0.001075621,0.001381558,0.002226489,0.0007783456,0.0009858537,0.00249285,0.001262103,0.001016823],"category_scores_gemma":[0.00834807,0.0005037452,0.001504646,0.002832805,0.0005769842,0.002481209,0.001155512,0.001313932,0.0006487658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004940745,"about_ca_system_score_gemma":0.000762808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00242414,"about_ca_topic_score_gemma":0.002537623,"domain_scores_codex":[0.9980773,0.0003921005,0.0001662815,0.0004022427,0.0008761123,0.00008582974],"domain_scores_gemma":[0.9978691,0.000819611,0.0002283783,0.0003343927,0.0006909419,0.00005752213],"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":[0.0003175532,0.0001723364,0.002890836,0.000332321,0.0004130963,0.0002316977,0.0002080533,0.2704089,0.02478555,0.02044402,0.004168104,0.6756275],"study_design_scores_gemma":[0.00002620993,0.0001621139,0.0008610917,0.00001833474,0.00009293787,0.0003037129,0.00004023821,0.9713224,0.01034369,0.008755671,0.008025794,0.00004792576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002632178,0.0003246031,0.9963839,0.00006656731,0.00005905394,0.00002473145,0.00002147548,0.0002360282,0.0002514211],"genre_scores_gemma":[0.1250866,0.0006168311,0.8720476,0.000176516,0.0001836696,0.0001609813,0.0002164621,0.0001129006,0.001398489],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00249285,"threshold_uncertainty_score":0.01161635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03673802789705223,"score_gpt":0.262177112267505,"score_spread":0.2254390843704527,"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."}}