{"id":"W2051184206","doi":"10.1109/tii.2015.2411231","title":"Lifetime and Energy Hole Evolution Analysis in Data-Gathering Wireless Sensor Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":212,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Wireless sensor network; Energy consumption; Computer science; Key distribution in wireless sensor networks; Initialization; Computer network; Real-time computing; Performance metric; Data collection; Mobile wireless sensor network; Sink (geography); Routing protocol; Energy (signal processing); Wireless network; Routing (electronic design automation); Wireless; Engineering; Telecommunications; Electrical engineering","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":[0.0006793722,0.0002456292,0.0003806996,0.0006538036,0.0001434485,0.0002411371,0.0007973085,0.0003469283,0.000003873579],"category_scores_gemma":[0.00001380789,0.0002478104,0.00007247383,0.002371058,0.00008130992,0.001134801,0.00002853565,0.0005455345,0.000007767639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014865,"about_ca_system_score_gemma":0.0001201621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004694557,"about_ca_topic_score_gemma":0.0003851597,"domain_scores_codex":[0.9979569,0.0001410113,0.0007357918,0.0003002121,0.0004394774,0.000426615],"domain_scores_gemma":[0.9982009,0.0001972691,0.0002236854,0.001036161,0.00008879996,0.0002532183],"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":[0.00002585041,0.00007064072,0.00006874913,0.00000220753,0.0001006209,0.000003684942,0.0004297507,0.9761006,0.000003236509,0.0004139226,0.0001592156,0.02262148],"study_design_scores_gemma":[0.000940973,0.00006616756,0.00001938823,0.00003515043,0.00009028191,0.000007237729,0.0002797423,0.9973744,0.0001576952,0.00001069287,0.000755503,0.0002628351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03760197,0.00002711082,0.9607766,0.0001126782,0.0008937889,0.00008790146,0.00001688469,0.0001594121,0.0003236278],"genre_scores_gemma":[0.9912826,0.00004469953,0.008225125,0.0001159541,0.0001201404,0.00001269437,0.00002174749,0.00001510095,0.0001619689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9536806,"threshold_uncertainty_score":0.9999974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04957215169558201,"score_gpt":0.2476666609815399,"score_spread":0.1980945092859579,"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."}}