{"id":"W2489311429","doi":"10.1109/icc.2016.7511526","title":"A relay subset selection scheme for Wireless Sensor Networks based on channel state information","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Relay; Relay channel; Channel state information; Computer science; Channel (broadcasting); Fading; Transmission (telecommunications); Computer network; Transmitter power output; Bit error rate; Wireless; Wireless sensor network; Binary symmetric channel; Topology (electrical circuits); Channel capacity; Power (physics); Telecommunications; Engineering; Electrical engineering; Physics; Transmitter","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":[0.0004654072,0.0002457001,0.0001991945,0.0002361235,0.0002095415,0.0001727517,0.0004637159,0.0001525816,0.0000115686],"category_scores_gemma":[0.00005126982,0.0001723997,0.0001041866,0.0005888841,0.00003600678,0.0009767838,0.00006445521,0.0001305668,0.000060318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443305,"about_ca_system_score_gemma":0.00005739853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001409858,"about_ca_topic_score_gemma":0.00002340371,"domain_scores_codex":[0.9981687,0.00008504579,0.0003898329,0.0004010306,0.0003572844,0.0005981065],"domain_scores_gemma":[0.9984444,0.0004251302,0.0002086231,0.0004950022,0.0002856415,0.0001412376],"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":[0.000103498,0.00006109487,0.0001397735,0.00001133631,0.00001312189,0.000001062513,0.00006130787,0.9599259,0.0003760334,0.009136201,0.004298242,0.02587246],"study_design_scores_gemma":[0.001116209,0.0002226028,0.0001976626,0.00007004674,0.000003134619,0.000003607393,0.000004974878,0.9899445,0.004574206,0.00005879531,0.00351431,0.0002899527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01953469,0.000004126797,0.9767743,0.001458062,0.0005957242,0.0004052962,0.000006896198,0.000591789,0.0006291419],"genre_scores_gemma":[0.9349364,0.00001493205,0.06182802,0.001913098,0.0001619054,0.0001157814,0.00002515128,0.00002691395,0.000977774],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9154018,"threshold_uncertainty_score":0.7030254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008666528161010755,"score_gpt":0.2066800952645177,"score_spread":0.1980135671035069,"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."}}