{"id":"W2059316910","doi":"10.1109/icuwb.2015.7324472","title":"Machine-to-Machine Communications in Cognitive Cellular Systems","year":2015,"lang":"en","type":"article","venue":"","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Cognitive radio; Computer science; Spectrum management; Machine to machine; Cognitive network; Inefficiency; Communications system; Resource allocation; Cognition; Computer network; Transmission (telecommunications); Distributed computing; Telecommunications; Wireless; Computer security; Internet of Things","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":[0.0002465668,0.00009238381,0.0001284206,0.00006804561,0.00002264285,0.00003349194,0.000213084,0.00004861603,0.0000294723],"category_scores_gemma":[0.0000163011,0.0000839063,0.00001730376,0.0002071959,0.00001200656,0.00005464152,0.00006973086,0.0001609905,0.0001929509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004338905,"about_ca_system_score_gemma":0.00001040624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004014769,"about_ca_topic_score_gemma":0.0002479748,"domain_scores_codex":[0.9994677,0.00004699524,0.0001688272,0.00007760918,0.00007966148,0.0001592239],"domain_scores_gemma":[0.9994645,0.00006508386,0.0000102903,0.000311166,0.00003775992,0.0001112562],"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":[0.0001191439,0.0003517338,0.02596408,0.0003055618,0.0001916375,0.00006431792,0.005379809,0.8666264,0.0008535458,0.02731502,0.0511089,0.02171987],"study_design_scores_gemma":[0.0006224437,0.00004119136,0.0001431559,0.0001069525,0.000005558204,0.000002947406,0.0001677844,0.9264382,0.0001749825,0.0001580177,0.07194418,0.0001946114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.008206952,0.008469955,0.2321857,0.000446774,0.000824591,0.01683245,0.00007451958,0.0009786988,0.7319803],"genre_scores_gemma":[0.9968582,0.00001677833,0.0005738966,0.00007350579,0.00005367103,0.001763267,0.000035923,0.0000215721,0.0006031729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9886513,"threshold_uncertainty_score":0.34216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05635932308727336,"score_gpt":0.2992535676615884,"score_spread":0.242894244574315,"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."}}