{"id":"W2408814043","doi":"10.1002/wcm.2679","title":"Improving CSMA/CA network performance under hidden collision","year":2016,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Collision; Idle; Channel (broadcasting); Throughput; Computer network; Carrier sense multiple access with collision avoidance; Hidden node problem; Computation; Transient (computer programming); State (computer science); Probabilistic logic; Computer security; Algorithm; Telecommunications; Wireless network; Wireless; Artificial intelligence; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004735459,0.0001734779,0.0002156226,0.00006889855,0.001073641,0.0002211702,0.0009816919,0.00006736163,0.00000333253],"category_scores_gemma":[0.00001074222,0.000134832,0.00005200614,0.0003840606,0.0001612616,0.000370162,0.001577082,0.0001820044,0.00001209326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006942709,"about_ca_system_score_gemma":0.00005637025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003408013,"about_ca_topic_score_gemma":0.00002419985,"domain_scores_codex":[0.9985899,0.0001452828,0.000322953,0.0003794413,0.000148635,0.0004137967],"domain_scores_gemma":[0.9976178,0.0006611232,0.0001708713,0.001316845,0.0001272919,0.000106052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002590884,0.00002815334,0.001044004,0.0000078685,0.00001212712,0.000001032085,0.0001900269,0.0005465001,0.0009073869,0.0170003,0.00008345518,0.9801766],"study_design_scores_gemma":[0.0003906609,0.00009411482,0.003791686,0.0003409451,0.000008692449,0.00004038624,0.00006897026,0.9906102,0.0002267679,0.0005856705,0.003562823,0.0002791093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.466028,0.001411101,0.5304191,0.0006492204,0.000165009,0.0002047576,0.000001155349,0.0001595992,0.0009620858],"genre_scores_gemma":[0.9623879,0.001556939,0.03564462,0.000157046,0.0001483161,0.00001288735,0.000003215194,0.00001610784,0.00007297429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9900637,"threshold_uncertainty_score":0.8257687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479313976286719,"score_gpt":0.2419248413496831,"score_spread":0.2271317015868159,"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."}}