{"id":"W4324373234","doi":"10.1002/dac.5467","title":"DR‐NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks","year":2023,"lang":"en","type":"article","venue":"International Journal of Communication Systems","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Wireless sensor network; Energy consumption; Routing (electronic design automation); Computer network; Node (physics); Sensor node; Data transmission; Key distribution in wireless sensor networks; Reduction (mathematics); Real-time computing; Wireless; Wireless network; Telecommunications; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00177008,0.0001555352,0.0002662792,0.0006708137,0.0001025183,0.0004431989,0.003938637,0.0001037265,0.000002133919],"category_scores_gemma":[0.00005219628,0.0001578348,0.00006014484,0.0009298094,0.00005467907,0.001616646,0.000599879,0.0004192597,0.000007349429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002776121,"about_ca_system_score_gemma":0.0001178571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002482539,"about_ca_topic_score_gemma":0.00003345852,"domain_scores_codex":[0.9969369,0.0006494772,0.00108804,0.0002627459,0.0008255735,0.0002373123],"domain_scores_gemma":[0.9965882,0.0002796529,0.001126461,0.001149287,0.0007792216,0.00007716681],"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":[0.00002634533,0.00005462428,0.0002337641,0.000001995227,0.00005088301,0.000026031,0.0004377308,0.9860761,0.0005166894,0.004512647,0.0002113298,0.007851836],"study_design_scores_gemma":[0.0004965899,0.0000296858,0.0007334684,0.0002257118,0.000007065596,0.0002381727,0.0008795743,0.9966558,0.00002617654,0.00006918378,0.0005025991,0.0001359449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4770181,0.001443357,0.514803,0.001453291,0.004582095,0.0002230659,0.000008070883,0.0001296629,0.0003393723],"genre_scores_gemma":[0.9954041,0.0007669869,0.003153947,0.00002394973,0.0004711932,0.000003888431,0.00009055399,0.00001958253,0.00006576577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5183861,"threshold_uncertainty_score":0.7319034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1156299970525185,"score_gpt":0.3354237571653605,"score_spread":0.219793760112842,"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."}}