{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001393887,0.0005239932,0.0005631807,0.0005700924,0.0006532295,0.0006906182,0.0007753238,0.0006179072,0.0007355269],"category_scores_gemma":[0.005661809,0.0001554262,0.0002239233,0.0005660858,0.0006232333,0.0008243129,0.0004132036,0.0005750037,0.00009520806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009466052,"about_ca_system_score_gemma":0.001569036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009702349,"about_ca_topic_score_gemma":0.005973941,"domain_scores_codex":[0.9992311,0.0001581816,0.00003301528,0.0001224393,0.0002434381,0.0002117899],"domain_scores_gemma":[0.9970149,0.001847105,0.0002670023,0.000285926,0.0004755681,0.0001096032],"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":[0.0006592319,0.0002265887,0.003721651,0.00005184513,0.00005333526,0.0001166595,0.00007723488,0.9336159,0.02290232,0.003724935,0.0005878781,0.03426242],"study_design_scores_gemma":[0.00001215413,0.00006393062,0.0002829451,0.000001519062,0.0000124667,0.00001265544,0.00000742799,0.9959797,0.003300566,0.000265553,0.0000576155,0.000003511733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7967505,0.0004127056,0.1963948,0.0002304177,0.0001118567,0.00003488827,0.00003692101,0.001357141,0.004670767],"genre_scores_gemma":[0.9930398,0.00004197363,0.006670553,0.00001282918,0.000005499858,0.000006241101,0.000009396294,0.000005132234,0.0002086199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009702349,"threshold_uncertainty_score":0.01929176,"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."}}