{"id":"W2080191567","doi":"10.1109/jcn.2014.000088","title":"HDRE: Coverage hole detection with residual energy in wireless sensor networks","year":2014,"lang":"en","type":"article","venue":"Journal of Communications and Networks","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Wireless sensor network; Residual; Energy (signal processing); Computer network; Key distribution in wireless sensor networks; Wireless; Quality of service; Real-time computing; Wireless network; Telecommunications; Algorithm; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0008856499,0.0001869863,0.0003453422,0.0002257862,0.000264149,0.000193579,0.001200097,0.0001626468,0.000001296531],"category_scores_gemma":[0.00001678382,0.000156233,0.00005592963,0.0007100697,0.0001573516,0.0004026596,0.0003340786,0.0006871046,3.342989e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005684277,"about_ca_system_score_gemma":0.00003657186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000477464,"about_ca_topic_score_gemma":0.0005367635,"domain_scores_codex":[0.9980916,0.0005432704,0.0005750913,0.0002068928,0.000259242,0.0003238637],"domain_scores_gemma":[0.9972734,0.0006822211,0.0005356665,0.001154994,0.0002150956,0.0001386639],"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":[0.00005512788,0.0001161686,0.0009544477,0.000002659022,0.00003012211,0.00001062091,0.00009839272,0.843091,0.00007925243,0.01121699,0.0001080409,0.1442372],"study_design_scores_gemma":[0.0008126119,0.0003140949,0.002191187,0.0001498061,0.00001615185,0.0001633729,0.00002554221,0.9916109,0.00007459318,0.0001688028,0.004275614,0.0001973185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07118509,0.001919359,0.9253081,0.0006146298,0.0001942227,0.00005262209,2.144721e-7,0.00003295227,0.0006928028],"genre_scores_gemma":[0.9824435,0.005204819,0.01174102,0.0002925456,0.000245898,0.000005411086,0.00000250005,0.00001977685,0.00004455258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9135671,"threshold_uncertainty_score":0.6370996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007946799263076766,"score_gpt":0.2114718371504416,"score_spread":0.2035250378873648,"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."}}