{"id":"W2119545161","doi":"10.1109/tmc.2010.254","title":"Lifetime Analysis of Random Event-Driven Clustered Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Voronoi diagram; Network packet; Probabilistic logic; Energy consumption; Event (particle physics); Real-time computing; Computer network; Wireless network; Wireless; Telecommunications; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001647004,0.0004723244,0.00038591,0.0007772534,0.000292507,0.0004217765,0.0009835885,0.0005936312,0.0006965986],"category_scores_gemma":[0.006662207,0.0003090132,0.0003951768,0.0005604004,0.0005259269,0.001186982,0.0004894585,0.0003683387,0.00009456203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008805647,"about_ca_system_score_gemma":0.0004433515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001269987,"about_ca_topic_score_gemma":0.0006458385,"domain_scores_codex":[0.9994836,0.0001879284,0.00002226063,0.00008247456,0.0001568243,0.00006689199],"domain_scores_gemma":[0.9974976,0.001466798,0.000387458,0.0001463134,0.0004191798,0.00008262276],"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":[0.000037347,0.00001792034,0.0008184745,0.00004612653,0.0000331127,0.00007881185,0.00005219981,0.9729034,0.002108294,0.01894635,0.0002486206,0.004709288],"study_design_scores_gemma":[0.000001727361,0.00001435367,0.0001924465,0.000002926642,0.000004565031,0.0000269704,0.00000987592,0.9953015,0.0002826529,0.004004519,0.0001550241,0.000003466579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1635812,0.001274097,0.8304946,0.0004042513,0.00003945046,0.00007111207,0.0001406036,0.0001770171,0.003817599],"genre_scores_gemma":[0.9818635,0.0007201117,0.01590494,0.00006226561,0.00002697491,0.0000683927,0.0001165857,0.00003029263,0.001207036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001647004,"threshold_uncertainty_score":0.008710325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160951400112579,"score_gpt":0.2362901031820996,"score_spread":0.2201949631708417,"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."}}