{"id":"W2905098398","doi":"10.1109/jiot.2018.2888502","title":"Machine-to-Machine Communications With Massive Access: Congestion Control","year":2018,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Random access; Scalability; Computer network; Access control; Distributed computing; Blocking (statistics); Access network; Telecommunications link; Integer programming; Radio access network; Access time; Algorithm; Base station","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":[],"consensus_categories":[],"category_scores_codex":[0.000329069,0.0001520573,0.0002277261,0.0001269569,0.00007839476,0.0001632288,0.0008615396,0.00006797408,0.0001629633],"category_scores_gemma":[0.00002273627,0.0001176803,0.00005956887,0.0001244957,0.0001038481,0.0004152145,0.00005836007,0.0005174668,0.00003298496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006185568,"about_ca_system_score_gemma":0.00002032007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008049791,"about_ca_topic_score_gemma":0.00004759179,"domain_scores_codex":[0.9991102,0.00005907768,0.0003458335,0.00009139109,0.0001868087,0.0002066377],"domain_scores_gemma":[0.9990578,0.00008793298,0.0001686141,0.0003167364,0.000240563,0.000128331],"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":[0.006229762,0.0008859314,0.05414428,0.0007501077,0.005688409,0.0002581672,0.02920232,0.3333934,0.04124297,0.006148335,0.2709244,0.251132],"study_design_scores_gemma":[0.002845578,0.001330731,0.001403594,0.001434964,0.0001169507,0.0004556527,0.00003783302,0.9412336,0.01202959,0.001302707,0.03730894,0.0004998363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03355717,0.0006599899,0.9424024,0.001939836,0.001573916,0.002679955,0.00002369399,0.000217783,0.01694527],"genre_scores_gemma":[0.9953331,0.00003977255,0.003556215,0.0004551726,0.0003274371,0.0001088015,0.000002358041,0.00003363419,0.0001434543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.961776,"threshold_uncertainty_score":0.4798863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792134565489666,"score_gpt":0.2848156502842569,"score_spread":0.2668943046293602,"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."}}