{"id":"W2356684428","doi":"10.1109/jiot.2016.2566659","title":"Distributed and Adaptive Medium Access Control for Internet-of-Things-Enabled Mobile Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer network; Computer science; Network packet; Superframe; Access control; Media access control; Throughput; Channel access method; Quality of service; Wireless; Telecommunications","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":[0.001284581,0.0002632035,0.0005950792,0.0001366334,0.00006677687,0.0003482865,0.001929431,0.0001786754,0.00003441513],"category_scores_gemma":[0.0001086928,0.0001725588,0.0002267641,0.0001542016,0.0001709937,0.002102439,0.0003264241,0.0003813915,0.00000127679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007569334,"about_ca_system_score_gemma":0.00008776334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006131516,"about_ca_topic_score_gemma":0.000002799633,"domain_scores_codex":[0.9977885,0.0001442315,0.0008603249,0.0003680059,0.0003610788,0.000477856],"domain_scores_gemma":[0.9970426,0.0007997802,0.00112099,0.0002852144,0.0005250684,0.0002263457],"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":[0.008506224,0.001099439,0.01265232,0.0006328868,0.003306851,0.0002464917,0.01216453,0.009768803,0.0191978,0.03674147,0.1365452,0.759138],"study_design_scores_gemma":[0.006559096,0.002498018,0.0004862522,0.002670859,0.00007932876,0.0002975955,0.00003898358,0.931712,0.03717539,0.01278213,0.005152262,0.0005481277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01053664,0.0002635909,0.9855241,0.0004785402,0.001182726,0.001888716,0.000007702973,0.00004126752,0.00007675819],"genre_scores_gemma":[0.991453,0.00005133557,0.007192934,0.0003115044,0.0003202175,0.0004223183,0.000001065222,0.00002244917,0.0002251609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9809164,"threshold_uncertainty_score":0.7036743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897436929804255,"score_gpt":0.2755538794875517,"score_spread":0.2565795101895091,"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."}}