{"id":"W1995742329","doi":"10.1145/1143549.1143783","title":"A new energy efficient approach by separating data collection and data report in wireless sensor networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wireless sensor network; Computer science; Key distribution in wireless sensor networks; Scalability; Cluster analysis; Data collection; Base station; Data aggregator; Computer network; Mobile wireless sensor network; Data transmission; Efficient energy use; Transmission (telecommunications); Wireless; Real-time computing; Wireless network; Engineering; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008981539,0.0003003016,0.0003496056,0.0001622729,0.0002104375,0.000408431,0.002208332,0.0001863702,0.000004008132],"category_scores_gemma":[0.00003285896,0.0002884316,0.00002222557,0.001415618,0.00005846157,0.0005208979,0.002559697,0.0002493996,0.000001148793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007617639,"about_ca_system_score_gemma":0.0001218063,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007544004,"about_ca_topic_score_gemma":0.001157782,"domain_scores_codex":[0.9961717,0.0001728692,0.0006856633,0.00188405,0.0004728517,0.0006128838],"domain_scores_gemma":[0.9957538,0.0001984849,0.0002574888,0.003586351,0.00005104725,0.0001528784],"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":[0.00001034627,0.0001927989,0.001157604,0.000006408954,0.00001336263,0.0001028361,0.00003295523,0.9002853,0.0002216353,0.00659418,0.08209877,0.009283789],"study_design_scores_gemma":[0.0004625904,0.00001863749,0.0002557994,0.00002231473,0.000007935411,0.0003202942,0.00002009543,0.9941544,0.0001352068,0.00001538984,0.004248609,0.0003387324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0147863,0.0005067781,0.9770529,0.0002079609,0.0003274788,0.0001676898,0.000006361704,0.0002695889,0.006674905],"genre_scores_gemma":[0.7359505,0.0000627372,0.2561001,0.0001757138,0.0003784407,0.00001134187,0.001576136,0.00004239621,0.005702584],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7211642,"threshold_uncertainty_score":0.9999568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01999944852348037,"score_gpt":0.2493662952533177,"score_spread":0.2293668467298374,"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."}}