{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007262763,0.000303358,0.000425502,0.0002956593,0.000117557,0.00009003132,0.0007695573,0.0001817764,0.00001469003],"category_scores_gemma":[0.00002088541,0.0002750845,0.00009564609,0.001114888,0.0001177754,0.00008686309,0.000606983,0.0001602482,0.000009471497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005310574,"about_ca_system_score_gemma":0.00003321189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002451786,"about_ca_topic_score_gemma":0.00005374918,"domain_scores_codex":[0.9972436,0.00008397998,0.000589291,0.0007412147,0.0005293658,0.0008125186],"domain_scores_gemma":[0.9979512,0.0004124177,0.0001649326,0.0008172539,0.0001951142,0.0004590721],"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":[0.0000298108,0.0001740554,0.0001116345,0.000007266327,0.00002981002,0.00005736324,0.0002509334,0.6887152,0.000658691,0.03428066,0.0004789781,0.2752056],"study_design_scores_gemma":[0.000429385,0.0001359614,0.0006752728,0.00004293118,0.000007679261,0.00004492019,0.00004732991,0.9913145,0.004882323,0.00001915588,0.00205596,0.000344578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05784677,0.0002225758,0.9378318,0.000284121,0.0005308921,0.0001017657,0.000001347535,0.0002367011,0.002943991],"genre_scores_gemma":[0.6727796,0.00003984714,0.3248139,0.0009455181,0.0001944067,0.000006423192,0.000002429444,0.0000312023,0.001186662],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6149328,"threshold_uncertainty_score":0.9999701,"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."}}