{"id":"W4405491046","doi":"10.1109/iccspa61559.2024.10794340","title":"HAPS-Enabled V2X Architecture for Hyper Reliable and Low-Latency Communication (HRLLC) in 6G Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Global Affairs Canada","keywords":"Computer science; Architecture; Latency (audio); Computer network; Low latency (capital markets); Computer architecture; 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":[0.0004369737,0.000426373,0.0002635577,0.0004225465,0.0005269171,0.001293248,0.001303781,0.0005682238,0.003311211],"category_scores_gemma":[0.0003950365,0.0001558212,0.0002459989,0.0002801503,0.0004918416,0.001172562,0.001548286,0.0007092052,0.001365638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006838197,"about_ca_system_score_gemma":0.0007571201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004381933,"about_ca_topic_score_gemma":0.004034847,"domain_scores_codex":[0.9996563,0.00006614321,0.00002126816,0.00007501851,0.00009697023,0.00008433432],"domain_scores_gemma":[0.9997811,0.00002444239,0.00001593785,0.00003909482,0.0001047863,0.0000347005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008510798,0.0002492666,0.005759872,0.0007507214,0.0002026348,0.001790459,0.002091692,0.146353,0.1490934,0.2733203,0.05495293,0.3645846],"study_design_scores_gemma":[0.00005037322,0.0009883374,0.002723705,0.0001633559,0.000111096,0.0008162666,0.000685081,0.7412794,0.04488919,0.03585692,0.1723281,0.0001080694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1219798,0.003556685,0.7789535,0.001914602,0.000936016,0.0006243587,0.0004233071,0.007461945,0.08414978],"genre_scores_gemma":[0.9319739,0.0009282505,0.04992212,0.0004845141,0.0001672433,0.0002035032,0.0004435635,0.00008584221,0.01579097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004381933,"threshold_uncertainty_score":0.01107711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007116163581363516,"score_gpt":0.2229333374704417,"score_spread":0.2158171738890782,"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."}}