{"id":"W2138374511","doi":"10.1002/dac.1384","title":"Energy‐efficient and localized lossy data aggregation in asynchronous sensor networks","year":2012,"lang":"en","type":"article","venue":"International Journal of Communication Systems","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Data aggregator; Wireless sensor network; Lossy compression; Asynchronous communication; Greedy algorithm; Redundancy (engineering); Scheduling (production processes); Algorithm; Mathematical optimization; Computer network; Mathematics; Artificial intelligence","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":[0.001233886,0.0003812129,0.0004277276,0.000412246,0.0004683073,0.0005084783,0.0008465596,0.00024362,0.0003386283],"category_scores_gemma":[0.002789719,0.0001847886,0.000186763,0.0004803432,0.0004483836,0.000884442,0.0006882331,0.0003395947,0.0001068907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004497843,"about_ca_system_score_gemma":0.0004040088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008109412,"about_ca_topic_score_gemma":0.001061777,"domain_scores_codex":[0.9993863,0.0001844808,0.0000351489,0.0001087357,0.0002279068,0.00005737667],"domain_scores_gemma":[0.9982626,0.0008370825,0.0003105362,0.0002852575,0.0002360354,0.00006853625],"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":[0.0006747884,0.0001653755,0.005048484,0.0001244566,0.00004921713,0.0002194349,0.0002227762,0.7816275,0.06025029,0.01079526,0.001497748,0.1393248],"study_design_scores_gemma":[0.0000142304,0.00007847299,0.0006882401,0.000003230487,0.000008037649,0.00004023296,0.0000190295,0.9881441,0.007383133,0.003159598,0.000456564,0.000005269264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1647608,0.0003189819,0.8334042,0.0001532812,0.00003644006,0.00002972058,0.00003273545,0.0003423911,0.0009215731],"genre_scores_gemma":[0.9370579,0.0001027873,0.06215985,0.00003180076,0.00002502313,0.00003559636,0.00003198118,0.00002223819,0.0005327442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001233886,"threshold_uncertainty_score":0.006525517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02573609463186236,"score_gpt":0.2784813748038594,"score_spread":0.252745280171997,"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."}}