{"id":"W2895854662","doi":"10.1109/bsc.2018.8494692","title":"Optimized Physical Carrier Sensing Threshold in High Density CSMA/CA Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Stochastic geometry; Rayleigh fading; Computer science; Throughput; Path loss; Node (physics); Channel (broadcasting); Interference (communication); Computer network; Poisson point process; Probability density function; Poisson distribution; Shadow mapping; Fading; Topology (electrical circuits); Wireless; Mathematics; Telecommunications; Engineering; Statistics","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":[0.00006757415,0.0001349827,0.0001968139,0.00007996234,0.00005430643,0.0000302867,0.0000954319,0.0001385222,0.00006394452],"category_scores_gemma":[0.00002087952,0.0001216335,0.00003963465,0.0002800491,0.00009474431,0.00008160547,0.00004960497,0.0001810558,0.00003832916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005342255,"about_ca_system_score_gemma":0.000006279726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008524378,"about_ca_topic_score_gemma":0.0001158904,"domain_scores_codex":[0.9993235,0.00000982055,0.0001427933,0.0001526244,0.00009063982,0.000280576],"domain_scores_gemma":[0.9996522,0.00002670322,0.00001186917,0.000228933,0.00004621212,0.00003408568],"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":[0.00005154131,0.00003413306,0.002321199,0.00002430958,0.00005604669,0.00004803642,0.000611095,0.9622569,0.001934236,0.01164502,0.008571058,0.01244642],"study_design_scores_gemma":[0.0003848978,0.00001620954,0.000728154,0.00001211887,0.000006562521,0.000003147136,0.00009031681,0.9598431,0.0375759,0.0009806077,0.000184177,0.0001748219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7164215,0.00002110619,0.2746525,0.00004697527,0.0004013795,0.0001015977,0.000001405359,0.0009691219,0.007384431],"genre_scores_gemma":[0.9957989,0.00001090126,0.003759479,0.00008510749,0.0002169185,0.000001731026,0.000005786455,0.00002394977,0.0000972571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2793773,"threshold_uncertainty_score":0.4960072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006897235838472175,"score_gpt":0.2081379201006103,"score_spread":0.2012406842621381,"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."}}