{"id":"W2117258774","doi":"10.1109/itng.2007.18","title":"A Quick and Energy Efficient Algorithm to Maximize Lifetime of Wireless Sensor Networks","year":2007,"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":"Acadia University","funders":"","keywords":"Wireless sensor network; Computer science; Schedule; Energy (signal processing); Key distribution in wireless sensor networks; Node (physics); Routing (electronic design automation); Energy consumption; Computer network; Algorithm; Real-time computing; Wireless network; Wireless; Engineering; Telecommunications; Electrical engineering; Mathematics","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":[0.001661523,0.001438921,0.00111154,0.001509152,0.0009125655,0.0008523453,0.001605149,0.001389783,0.002517235],"category_scores_gemma":[0.004061349,0.0006240166,0.000609797,0.001448189,0.0009049144,0.002700602,0.001262044,0.001045896,0.0008277549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008502209,"about_ca_system_score_gemma":0.001950862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009055004,"about_ca_topic_score_gemma":0.001934832,"domain_scores_codex":[0.9992301,0.0002132465,0.00004280006,0.0001572599,0.0002734312,0.00008321137],"domain_scores_gemma":[0.998902,0.0005975388,0.0001237085,0.0001033457,0.0002047597,0.00006865861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003782772,0.0002080926,0.0006443436,0.0004202511,0.0001393433,0.0001530119,0.0002391691,0.385714,0.02358492,0.03411703,0.01511624,0.5392852],"study_design_scores_gemma":[0.0002953672,0.0003701979,0.0003704843,0.00006387567,0.00006450147,0.0003471527,0.00007713508,0.9312489,0.0167103,0.03121416,0.01917795,0.00006002282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007834919,0.0009791277,0.9872568,0.0002906522,0.0001419812,0.0001880513,0.00006431115,0.001115963,0.00212831],"genre_scores_gemma":[0.08119476,0.0006296778,0.9137346,0.0001306021,0.00007132239,0.0005169737,0.0001702853,0.0001707598,0.003380968],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002517235,"threshold_uncertainty_score":0.008787096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005549963533575824,"score_gpt":0.2092015603650807,"score_spread":0.2036515968315049,"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."}}