{"id":"W3187977197","doi":"10.1109/icc42927.2021.9500930","title":"Random Access with and without Sensing in Non-Terrestrial Networks for Timely Updates","year":2021,"lang":"en","type":"article","venue":"","topic":"Age of Information Optimization","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Research and Development; National Natural Science Foundation of China","keywords":"Aloha; Computer science; Computer network; Random access; Network packet; Markov chain; Performance metric; Transmission (telecommunications); Latency (audio); Real-time computing; Propagation delay; Metric (unit); Stochastic geometry; Wireless; Throughput; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001595515,0.00006931905,0.0001132325,0.00005134474,0.00005540841,0.0004644528,0.0001357009,0.00003415483,0.000005397719],"category_scores_gemma":[0.00002507355,0.00005467959,0.00001210881,0.0002219499,0.00001550849,0.001323631,0.0001075366,0.00004504037,0.000001088674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001138881,"about_ca_system_score_gemma":0.00005061285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001477991,"about_ca_topic_score_gemma":0.00005935158,"domain_scores_codex":[0.9994687,0.00001899147,0.0001605541,0.0001468039,0.00008184357,0.0001230729],"domain_scores_gemma":[0.9996241,0.00007309302,0.00006238421,0.0001392904,0.00006817506,0.00003290656],"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":[0.0004270106,0.00002716568,0.003578017,0.00002181807,0.00003027133,0.0000149116,0.0008560062,0.9321861,0.00002515264,0.001755334,0.001634277,0.05944391],"study_design_scores_gemma":[0.003222287,0.00001875464,0.0003536781,0.00002460036,0.00000265773,0.0000184575,0.00002201641,0.9954677,0.0006115424,0.00008123265,0.00009109893,0.00008600585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01179226,0.00001153749,0.9860348,0.000448123,0.0000987661,0.0002231232,2.24849e-7,0.00003920163,0.001352004],"genre_scores_gemma":[0.6001936,0.00001079953,0.3991348,0.0004256236,0.00005683312,0.00000544788,0.00001583116,0.000005564679,0.0001514318],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5884014,"threshold_uncertainty_score":0.4478729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01257246547711116,"score_gpt":0.2552784125380375,"score_spread":0.2427059470609263,"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."}}