{"id":"W3108959094","doi":"10.1007/978-3-030-60382-3_8","title":"A Distributed Contention-Resolution Self-Organizing TDMA Scheme for MTC","year":2020,"lang":"en","type":"book-chapter","venue":"Wireless networks","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Time division multiple access; Computer science; Computer network; Channel (broadcasting); Transmission (telecommunications); Throughput; Queue; Real-time computing; Distributed computing; Wireless; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003464847,0.0002905913,0.0003898281,0.0004523294,0.0006743493,0.0005609473,0.00148263,0.000521595,0.003636635],"category_scores_gemma":[0.000966944,0.0001556463,0.0001961553,0.0007040064,0.0003884316,0.0005544963,0.0005462304,0.0007266759,0.000788914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006186087,"about_ca_system_score_gemma":0.0005971771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001316464,"about_ca_topic_score_gemma":0.001828123,"domain_scores_codex":[0.9997701,0.00004281486,0.00001097523,0.00003509168,0.0001186567,0.00002230163],"domain_scores_gemma":[0.9996257,0.0001137376,0.0000176889,0.0001126229,0.0001038772,0.0000263808],"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":[0.0005135025,0.0002077139,0.000297063,0.0001857937,0.00005504916,0.0001420449,0.0001759812,0.0711758,0.1195671,0.08596639,0.01129256,0.710421],"study_design_scores_gemma":[0.00008454633,0.0002553509,0.0003846816,0.0000229799,0.0000402164,0.0004098522,0.00002977011,0.9225103,0.0277291,0.01619096,0.03230214,0.00004017209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0126584,0.0009118702,0.9717873,0.0001413074,0.000353139,0.0001653774,0.00006860754,0.001161599,0.01275244],"genre_scores_gemma":[0.5249641,0.0006712945,0.4481751,0.0002107502,0.0002798317,0.0002984443,0.000178375,0.0001775815,0.02504439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003636635,"threshold_uncertainty_score":0.01216573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02526847462910639,"score_gpt":0.233172126380191,"score_spread":0.2079036517510846,"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."}}