{"id":"W4402129557","doi":"10.1109/tcomm.2024.3453399","title":"Rate Adaptation in Delay-Sensitive and Energy-Constrained Large-Scale IoT Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"King Abdullah University of Science and Technology","keywords":"Computer science; Adaptation (eye); Scale (ratio); Energy (signal processing); Computer network; Electronic engineering; Engineering; Mathematics; Statistics; Physics","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":[0.001834524,0.0008654799,0.0008827645,0.000559481,0.0006088649,0.0011355,0.001534186,0.0009737697,0.000564275],"category_scores_gemma":[0.005575821,0.0005137559,0.0005433637,0.0007203656,0.002100332,0.001650289,0.001102621,0.0009116443,0.0001058881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002083508,"about_ca_system_score_gemma":0.0009616265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006139049,"about_ca_topic_score_gemma":0.002219826,"domain_scores_codex":[0.998749,0.0003728429,0.00005479209,0.0003103589,0.0003159394,0.000197004],"domain_scores_gemma":[0.9968467,0.001853206,0.0006624841,0.000204693,0.0003048407,0.0001280238],"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":[0.00004185614,0.00002342386,0.0003535382,0.00005543716,0.00002100254,0.0001502493,0.00009758089,0.965221,0.003611859,0.02665185,0.0002299231,0.003542397],"study_design_scores_gemma":[0.000003150359,0.00001764942,0.0001512179,0.00000320306,0.000006083108,0.00002166104,0.00001367792,0.9943148,0.0002887662,0.005054844,0.0001179783,0.00000685448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09055882,0.00162144,0.9035962,0.0004149804,0.0001146913,0.00007341336,0.00009002964,0.000280812,0.003249646],"genre_scores_gemma":[0.9873848,0.0007510483,0.01048366,0.00005578734,0.00005394712,0.00006118637,0.00002332541,0.00003174029,0.001154562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006139049,"threshold_uncertainty_score":0.01511699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671568903830576,"score_gpt":0.2418979535470008,"score_spread":0.2251822645086951,"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."}}