{"id":"W4391708761","doi":"10.1109/icc51166.2024.10622623","title":"RSCNet: Dynamic CSI Compression for Cloud-Based WiFi Sensing","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of the Fraser Valley; York University","funders":"","keywords":"Cloud computing; Computer science; Compression (physics); Remote sensing; Real-time computing; Geology; Operating system; Materials science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004112569,0.0005653538,0.0005512873,0.0004302533,0.00018361,0.0007466543,0.00161258,0.0005707851,0.00001127197],"category_scores_gemma":[0.00002996039,0.0004998951,0.0004089811,0.0004530352,0.00008225599,0.00006169012,0.002949093,0.0009831153,0.00006080043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000226988,"about_ca_system_score_gemma":0.0002749584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005022073,"about_ca_topic_score_gemma":0.00005041545,"domain_scores_codex":[0.9965091,0.0001295899,0.0005593825,0.001599465,0.0005313598,0.0006710725],"domain_scores_gemma":[0.9970641,0.0004583551,0.0002357424,0.001854525,0.0002150285,0.0001722553],"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":[0.00001308348,0.00004849663,0.000001673065,0.0003411374,0.00004211546,0.00003416061,0.00008800704,0.9421497,0.0008599627,0.02217537,0.004703328,0.02954297],"study_design_scores_gemma":[0.0002675564,0.00004017181,0.000008116259,0.000984121,0.0000337553,0.000007949042,0.000005467684,0.9824513,0.00240189,0.007974518,0.005229237,0.0005959368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008803845,0.0007831005,0.9723159,0.001829563,0.009446294,0.0006838186,0.0000193899,0.001611805,0.004506285],"genre_scores_gemma":[0.6081006,0.00001778573,0.3868321,0.000637111,0.0004639435,0.00003155131,0.0001242554,0.00009953326,0.003693141],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5992967,"threshold_uncertainty_score":0.9997452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0158808210436568,"score_gpt":0.2693397064369012,"score_spread":0.2534588853932445,"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."}}