{"id":"W4253159153","doi":"10.32920/ryerson.14665536","title":"Machine To Machine Overlay Network Over Random Access Channel Of LTE For Smart City","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Alfaisal University","keywords":"Computer science; Computer network; Random access; Carrier sense multiple access with collision avoidance; Physical layer; Access control; Overlay; Context (archaeology); Overlay network; Wireless; Throughput; Telecommunications; Operating system","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":[0.0004610687,0.0002586133,0.0003505437,0.0003780307,0.000708886,0.0006786715,0.0005340485,0.0003780136,0.001044198],"category_scores_gemma":[0.001000316,0.00007515018,0.0002027167,0.0003253989,0.0002849775,0.0007077786,0.0006647918,0.0002516202,0.0001903031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007677583,"about_ca_system_score_gemma":0.0006768735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004170256,"about_ca_topic_score_gemma":0.005814034,"domain_scores_codex":[0.9995601,0.0001568386,0.0000173666,0.00005895789,0.00009030951,0.0001163838],"domain_scores_gemma":[0.9995216,0.0001446892,0.0000614264,0.0000855471,0.0001444411,0.00004229666],"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":[0.001972276,0.0005059036,0.0166346,0.0004079902,0.0001521068,0.002123478,0.001105427,0.4389108,0.1166596,0.08072875,0.01515239,0.3256467],"study_design_scores_gemma":[0.00002281861,0.0003683017,0.002209971,0.00001111577,0.00004625793,0.0002986382,0.0002191133,0.9770185,0.00808651,0.003806444,0.007885454,0.00002686473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6515444,0.001188902,0.3296858,0.0005445139,0.0001753296,0.000214264,0.0001537466,0.002213073,0.0142799],"genre_scores_gemma":[0.9872835,0.0001181858,0.01164959,0.00003261784,0.0000134359,0.00002658877,0.00006212565,0.00000588748,0.0008081705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004170256,"threshold_uncertainty_score":0.008292019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01819462887173093,"score_gpt":0.2620863070388065,"score_spread":0.2438916781670755,"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."}}