{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000227807,0.0001137111,0.0001535837,0.00009936111,0.00014969,0.00006081087,0.0006196569,0.00009461435,0.00001002766],"category_scores_gemma":[0.0001229145,0.0001073173,0.00006987809,0.0002818658,0.00008379827,0.0001443919,0.0001799371,0.00008630187,8.563164e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001531741,"about_ca_system_score_gemma":0.00002888124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001079624,"about_ca_topic_score_gemma":0.00012555,"domain_scores_codex":[0.9990289,0.00004745066,0.0002882114,0.0002502305,0.0001799001,0.0002053221],"domain_scores_gemma":[0.998199,0.0004764893,0.0001787161,0.0008342077,0.0002609289,0.00005063795],"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":[0.000005467247,0.00005596325,0.00006247533,0.000005517856,0.000005445194,9.156032e-8,0.0001176553,0.7989934,0.008635201,0.1903908,0.00007117559,0.001656767],"study_design_scores_gemma":[0.0002998264,0.00002484257,0.00007657625,0.00001336022,0.000003131156,0.0000022706,0.00003125294,0.9725687,0.02487625,0.00006311201,0.001917767,0.0001229255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03605929,0.00003319593,0.9615297,0.0002870452,0.0003383022,0.0001237945,0.000001964702,0.0001660188,0.001460727],"genre_scores_gemma":[0.5701615,0.000009219116,0.4295792,0.00005707822,0.00002130216,0.00001575711,0.00001071311,0.000007892701,0.0001372907],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5341023,"threshold_uncertainty_score":0.4376273,"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."}}