{"id":"W2989301990","doi":"10.1109/jiot.2019.2951584","title":"Energy- and Delay-Aware Two-Hop NOMA-Enabled Massive Cellular IoT Communications","year":2019,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Energy consumption; Node (physics); Quality of service; Efficient energy use; Cellular network; Stochastic geometry; Cluster analysis; Distributed computing; Queueing theory; Transmission delay; Network performance; Transmission (telecommunications); Queuing delay; Network packet; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003982565,0.0004857317,0.0004157275,0.0003606255,0.0005720655,0.0007655082,0.0006659359,0.0004833791,0.0006797988],"category_scores_gemma":[0.001147789,0.000266817,0.0003246723,0.0005873361,0.0004331521,0.0007884256,0.0007851757,0.000534407,0.000157191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007094655,"about_ca_system_score_gemma":0.0008155443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002500533,"about_ca_topic_score_gemma":0.006610444,"domain_scores_codex":[0.9997546,0.00007306077,0.00000956187,0.00004352381,0.00006729994,0.00005199494],"domain_scores_gemma":[0.9995108,0.0002344715,0.00008324299,0.00004377763,0.00009198567,0.00003577315],"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":[0.00008585898,0.00007606228,0.0008146609,0.00007200703,0.00002499356,0.0002113171,0.00008428372,0.9333595,0.01259183,0.03209991,0.0009271281,0.01965242],"study_design_scores_gemma":[0.000002357955,0.00002546988,0.00009406269,0.000001738425,0.000003864537,0.00002264076,0.00001175818,0.9970996,0.0004095182,0.00211875,0.0002057053,0.000004424739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08193741,0.0003238786,0.9128432,0.0002061994,0.00009162794,0.0000464273,0.00007664948,0.0001520973,0.004322495],"genre_scores_gemma":[0.9674846,0.0001979795,0.03060132,0.00005320903,0.00002713386,0.00003563924,0.00003071223,0.00001076818,0.001558686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002500533,"threshold_uncertainty_score":0.005147517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01158651260105516,"score_gpt":0.2307919382566957,"score_spread":0.2192054256556406,"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."}}