{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001847324,0.0001398181,0.0002529122,0.0003441549,0.00006702195,0.0002769112,0.002889537,0.00009981972,0.000002856186],"category_scores_gemma":[0.00009001965,0.0001289046,0.00004320646,0.000294153,0.00007542474,0.0009058616,0.0007981098,0.00027286,0.00000318298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000193154,"about_ca_system_score_gemma":0.00005867481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001422771,"about_ca_topic_score_gemma":0.00001981366,"domain_scores_codex":[0.9975004,0.0005577859,0.000866286,0.0001764511,0.0006654636,0.0002336281],"domain_scores_gemma":[0.9969686,0.0004574625,0.0008650529,0.001080538,0.0004995621,0.0001287335],"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":[0.00002906266,0.0002061749,0.004862029,0.000002044863,0.00007049612,0.00001145965,0.000445219,0.9370074,0.00002988512,0.03431878,0.0002767162,0.02274076],"study_design_scores_gemma":[0.0006414978,0.00001894062,0.001478014,0.0002536927,0.000007030465,0.0003209127,0.0001111156,0.9818649,0.00003799595,0.00002878177,0.01511033,0.0001268031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04511307,0.0208401,0.930465,0.0008035796,0.002127903,0.00008519444,0.000003218186,0.0000308642,0.0005310153],"genre_scores_gemma":[0.9872946,0.001951258,0.01021052,0.0001073097,0.0003700263,0.000003902145,0.00002372185,0.00001227496,0.00002635278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9421816,"threshold_uncertainty_score":0.5369526,"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."}}