{"id":"W4311628423","doi":"10.3390/s22239455","title":"Data Freshness and End-to-End Delay in Cross-Layer Two-Tier Linear IoT Networks","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Age of Information Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université du Québec à Montréal","funders":"Université du Québec à Montréal","keywords":"Computer science; Network packet; Computer network; Quality of service; End-to-end principle; End-to-end delay; Relay; Metric (unit); Performance metric; Base station; Network performance; Queuing delay; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005590615,0.0001080408,0.0001138712,0.0001636672,0.0002178882,0.0001703591,0.0009072827,0.00003480061,0.0001453144],"category_scores_gemma":[0.00005578169,0.0001139206,0.00001396117,0.0005926372,0.00003391968,0.0005668919,0.001429246,0.0002158004,0.00002699171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005342253,"about_ca_system_score_gemma":0.00004160325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007251064,"about_ca_topic_score_gemma":0.00007593935,"domain_scores_codex":[0.9987596,0.00008517484,0.0002753407,0.0003315899,0.0003028943,0.0002454175],"domain_scores_gemma":[0.9989251,0.00007189695,0.00008661133,0.0007792713,0.00005232744,0.00008480877],"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":[0.000008566266,0.00001225234,0.001296844,0.000002365451,0.000004017646,0.00001549756,0.001089053,0.9908872,0.00000390944,0.0007346621,0.0013415,0.004604107],"study_design_scores_gemma":[0.0003284401,0.00002269784,0.001938708,0.000003984765,0.000001549445,0.00002438527,0.00009063511,0.9788754,0.00002498961,0.00003202883,0.0185172,0.0001399626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5840585,0.00009281049,0.4118794,0.001114583,0.0006430884,0.0003450328,0.00006209702,0.0001496067,0.001654854],"genre_scores_gemma":[0.9302964,0.000009147728,0.06728306,0.001556451,0.00009772515,0.00001417038,0.0001078124,0.00001538575,0.0006198997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3462378,"threshold_uncertainty_score":0.4645548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02764264448881245,"score_gpt":0.2919493302832707,"score_spread":0.2643066857944583,"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."}}