{"id":"W4360585398","doi":"10.1109/ic3i56241.2022.10073380","title":"Data Reduction Techniques in Wireless Sensor Networks with AI","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Wireless sensor network; Computer science; Reduction (mathematics); Data reduction; Resource (disambiguation); Wireless; Data collection; Key distribution in wireless sensor networks; Volume (thermodynamics); Real-time computing; Computer network; Data mining; Distributed computing; Wireless network; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001635995,0.0008128745,0.0008893971,0.001593786,0.0006916401,0.001444503,0.001872924,0.0008825441,0.002424972],"category_scores_gemma":[0.00477597,0.0003911846,0.0009224477,0.00387335,0.00123405,0.002356233,0.001614379,0.001976581,0.001300547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006674048,"about_ca_system_score_gemma":0.0008356582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00213419,"about_ca_topic_score_gemma":0.001833088,"domain_scores_codex":[0.998244,0.0005055948,0.0001422938,0.0002429646,0.0007969324,0.00006818334],"domain_scores_gemma":[0.9983374,0.0009161003,0.0001280912,0.0002533865,0.0003385209,0.00002662767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009695218,0.00007681407,0.0006067827,0.001865868,0.0001464607,0.0003069435,0.0003445484,0.09399762,0.006289848,0.1573312,0.01366713,0.7252699],"study_design_scores_gemma":[0.0000387725,0.0001707476,0.0007845182,0.0004206141,0.00008586057,0.0009287097,0.0003029242,0.5683455,0.009832901,0.2530451,0.1659661,0.00007821168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001276638,0.0152703,0.9763341,0.0008014436,0.0003952825,0.000093233,0.0001086892,0.0003738604,0.005346436],"genre_scores_gemma":[0.0882249,0.03304262,0.864765,0.0007550864,0.00101457,0.0005639722,0.0005076735,0.0001790009,0.01094713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002424972,"threshold_uncertainty_score":0.008652091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01726034292540423,"score_gpt":0.2439044480997556,"score_spread":0.2266441051743514,"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."}}