{"id":"W1980165860","doi":"10.1109/bsc.2010.5472916","title":"Optimization of multiple overlapping queries for energy efficient sensor communication","year":2010,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Flooding (psychology); Energy consumption; Routing (electronic design automation); Query optimization; Wireless sensor network; Distributed computing; Constraint (computer-aided design); Efficient energy use; Online aggregation; Routing protocol; Energy (signal processing); Time constraint; Computer network; Data mining; Sargable; Web search query; Information retrieval; Search engine","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":[0.00209965,0.0009300693,0.001511179,0.0004472982,0.000563173,0.0009369088,0.001477459,0.0009476217,0.0009670505],"category_scores_gemma":[0.003852489,0.0004596552,0.000538657,0.00105991,0.0006874347,0.0021021,0.001274338,0.0007718612,0.0001223234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007715737,"about_ca_system_score_gemma":0.0009089412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001367199,"about_ca_topic_score_gemma":0.001473708,"domain_scores_codex":[0.9983658,0.0005584811,0.00009576014,0.0003069762,0.0004503939,0.000222598],"domain_scores_gemma":[0.9979731,0.001443389,0.0002106468,0.0001317984,0.0001483924,0.00009264301],"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":[0.00040617,0.0001665944,0.0009329816,0.0001244039,0.0000666031,0.0001875967,0.0002064657,0.9208946,0.01355598,0.01009807,0.001278842,0.05208171],"study_design_scores_gemma":[0.00002318206,0.0001114136,0.0001841297,0.000002187612,0.00001475967,0.00005534342,0.00006059498,0.9950699,0.001520882,0.002595671,0.0003549166,0.000007008534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.140637,0.0008181134,0.8559447,0.0004170101,0.00002904915,0.0001328126,0.00007056867,0.0003364714,0.001614203],"genre_scores_gemma":[0.9153935,0.0002616818,0.08269754,0.00007178567,0.00004071752,0.0001486567,0.00008153099,0.00006337142,0.001241219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00209965,"threshold_uncertainty_score":0.01110411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009436674220551033,"score_gpt":0.2178445242270338,"score_spread":0.2084078500064828,"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."}}