{"id":"W4297336502","doi":"10.48550/arxiv.1606.02316","title":"Decentralized AP Selection in Large-Scale Wireless LANs Considering\\n Multi-AP Interference","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Telecommunications link; Interference (communication); Computer science; Signal-to-interference-plus-noise ratio; Throughput; Computer network; Selection algorithm; Channel (broadcasting); Signal-to-noise ratio (imaging); Wireless network; Selection (genetic algorithm); Wireless; Telecommunications; Power (physics); Physics","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":[0.0003525129,0.0004016048,0.0005032873,0.0002792241,0.00013435,0.0001748057,0.001610437,0.0004341849,0.00006697429],"category_scores_gemma":[0.00001772736,0.0004077412,0.0001808704,0.0005774812,0.00009169421,0.0004322235,0.001683583,0.0007425508,0.0000670986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003201019,"about_ca_system_score_gemma":0.0002497861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001645079,"about_ca_topic_score_gemma":0.001697393,"domain_scores_codex":[0.9972788,0.0003028216,0.000329588,0.00129109,0.00009858623,0.000699097],"domain_scores_gemma":[0.9984913,0.000142354,0.0002874181,0.0007333229,0.0001436464,0.0002019113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007599049,0.00192829,0.4919925,0.0009428072,0.0004593693,0.001656233,0.005683314,0.2043324,0.001909849,0.2737535,0.002188564,0.01439329],"study_design_scores_gemma":[0.002680916,0.00006983139,0.008838177,0.000927362,0.00002377108,0.00001014005,0.00006186732,0.9712097,0.0009319932,0.01300399,0.001395402,0.0008468605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1598692,0.00002873104,0.8369088,0.0001001065,0.0004063845,0.001947675,0.00001552413,0.0002698768,0.0004536821],"genre_scores_gemma":[0.9962194,0.0001599305,0.002758385,0.00008608736,0.00006805835,0.00005510026,0.000006029516,0.00002414861,0.0006228489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8363502,"threshold_uncertainty_score":0.9998375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06630661042503136,"score_gpt":0.2218203372557291,"score_spread":0.1555137268306978,"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."}}