{"id":"W2617721189","doi":"10.1109/mnet.2017.1600134","title":"Adapting LTE/LTE-A to M2M and D2D Communications","year":2017,"lang":"en","type":"article","venue":"IEEE Network","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"LTE Advanced; Handshake; Computer network; Computer science; Telecommunications link; Cellular network; Random access; Overlay; Term (time); 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":[0.0004103857,0.0004970618,0.0002534051,0.0003316657,0.0004721608,0.0006934188,0.0006104131,0.0008012047,0.0004249314],"category_scores_gemma":[0.0009282636,0.0001458268,0.0002141663,0.0004269,0.0003449849,0.0009749286,0.001000337,0.0005628797,0.0003634234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005393369,"about_ca_system_score_gemma":0.0004959298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005916109,"about_ca_topic_score_gemma":0.005756495,"domain_scores_codex":[0.999599,0.00008221676,0.00002081018,0.0000538723,0.0001277897,0.0001162617],"domain_scores_gemma":[0.999642,0.00006922679,0.00002007937,0.0000961845,0.0001388989,0.00003358249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005605408,0.0002635174,0.009531807,0.0002358429,0.0001184543,0.001885034,0.0003792068,0.2416664,0.243538,0.03938078,0.009101648,0.4533387],"study_design_scores_gemma":[0.00003121863,0.0002616695,0.003383111,0.00002553556,0.00005660436,0.001225412,0.0002413407,0.9291871,0.02810823,0.01228799,0.02513684,0.0000550292],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2587474,0.003667261,0.6999399,0.0008997511,0.000680986,0.000194185,0.0001325247,0.001950931,0.03378711],"genre_scores_gemma":[0.9404307,0.001048546,0.05543263,0.0002375975,0.0001460104,0.00003937195,0.0000781553,0.00003719502,0.002549941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005916109,"threshold_uncertainty_score":0.01176339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04288766625645485,"score_gpt":0.2951940137208894,"score_spread":0.2523063474644346,"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."}}