{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005331552,0.0001633598,0.0001783206,0.0001925081,0.0002213943,0.0001127967,0.001982211,0.00005215911,0.00003547298],"category_scores_gemma":[0.000003900702,0.0001470823,0.00001959156,0.001340067,0.00005420044,0.0005230437,0.001637604,0.0005137661,0.000002431309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001193292,"about_ca_system_score_gemma":0.00005895918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001785663,"about_ca_topic_score_gemma":0.0001204176,"domain_scores_codex":[0.9979545,0.0001997644,0.0002487474,0.0007755865,0.0004213268,0.0004000913],"domain_scores_gemma":[0.9980273,0.00005107563,0.00009079822,0.001725659,0.00004292196,0.00006226653],"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":[0.00003026745,0.0001796264,0.0005582808,0.000003471793,0.0000109665,0.00009320045,0.00009565476,0.9429034,0.0003210006,0.01892618,0.006991032,0.02988693],"study_design_scores_gemma":[0.0001826627,0.0001111953,0.0001164206,0.00001429745,0.000002851859,0.0001836882,0.00008894492,0.9896716,0.0007519784,0.00003326338,0.008595059,0.0002480421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02286416,0.00005558494,0.9703104,0.001835216,0.0004113269,0.0002630843,0.000004399221,0.0007914358,0.003464428],"genre_scores_gemma":[0.9169996,0.00002288841,0.08133779,0.0005726522,0.0001283222,0.00005890822,0.00006019692,0.000024244,0.0007954207],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8941354,"threshold_uncertainty_score":0.5997841,"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."}}