{"id":"W2990179824","doi":"10.1109/tvt.2019.2956038","title":"Energy Harvesting Wireless Sensor Networks With Channel Estimation: Delay and Packet Loss Performance Analysis","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Age of Information Optimization","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Network packet; Wireless sensor network; Channel (broadcasting); Computer science; Channel state information; Sink (geography); Context (archaeology); Real-time computing; Performance metric; Node (physics); Energy harvesting; Packet loss; Energy (signal processing); Computer network; Wireless; Engineering; Telecommunications; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001319869,0.0001999963,0.0002538121,0.0008044355,0.0002475671,0.0001030709,0.0003379983,0.0002341619,0.00001209472],"category_scores_gemma":[0.000001950649,0.0001811962,0.00005457654,0.002198568,0.0001125467,0.0008748688,0.00000716451,0.0002617481,0.00001986056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005360942,"about_ca_system_score_gemma":0.00003173135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001968894,"about_ca_topic_score_gemma":0.00003061463,"domain_scores_codex":[0.9988263,0.00003167866,0.0002746129,0.00037291,0.0002175691,0.0002769322],"domain_scores_gemma":[0.9990102,0.00004182285,0.0001450378,0.0005791933,0.0001632677,0.00006048723],"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":[0.000009703769,0.00002463447,0.0002359152,0.00001073739,0.0001298525,0.000008262287,0.00006792037,0.9613597,0.00002106209,0.0007245338,0.000001805744,0.03740582],"study_design_scores_gemma":[0.0003600866,0.0001756332,0.0001619979,0.00003406202,0.00007410718,0.0001503859,0.00003952075,0.9946827,0.004038188,0.00002174472,0.00004005967,0.0002214966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2223212,0.00001893156,0.7765983,0.0004087905,0.00008381368,0.0001203073,0.000001564418,0.0003815737,0.00006549702],"genre_scores_gemma":[0.9437834,0.00006334957,0.05575771,0.0001438035,0.000007027158,0.00004220639,0.000006414016,0.00001339982,0.000182673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7214622,"threshold_uncertainty_score":0.7388968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004040957086162419,"score_gpt":0.181070330876305,"score_spread":0.1770293737901426,"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."}}