{"id":"W4415626617","doi":"10.1109/tccn.2025.3626342","title":"Adaptive Multi-Dimensional Resource Slicing in Cognitive Satellite-Terrestrial Vehicular Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Satellite Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unicast; Multicast; Slicing; Resource allocation; Resource management (computing); Vehicular ad hoc network; Cognitive radio; Base station","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":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001430518,0.0009097245,0.001042512,0.001172294,0.001621636,0.0003158334,0.001041731,0.0007227091,0.00003651034],"category_scores_gemma":[0.00004468005,0.00112328,0.0003404325,0.002542233,0.0008762962,0.0003781873,0.00009810043,0.003089113,0.00003066199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004632083,"about_ca_system_score_gemma":0.0001786783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003141593,"about_ca_topic_score_gemma":0.001292698,"domain_scores_codex":[0.9937565,0.002148144,0.001757203,0.0009852784,0.0003784422,0.0009743776],"domain_scores_gemma":[0.9885063,0.00872779,0.0003684814,0.001759214,0.0003799913,0.0002582146],"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":[0.001096443,0.001103291,0.0009914318,0.0001113662,0.001369077,0.00002177546,0.002942172,0.06367929,0.0001080389,0.000123003,0.00002089876,0.9284332],"study_design_scores_gemma":[0.005963327,0.0002022937,0.001295252,0.01622338,0.0006604448,0.00002301508,0.005088174,0.9608785,0.0003258964,0.00006072285,0.008157535,0.001121492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0100308,0.2218106,0.7539238,0.0004249807,0.002257235,0.003359441,0.0001569344,0.0003902581,0.007645994],"genre_scores_gemma":[0.8979231,0.09932192,0.001136201,0.0004369421,0.0001750575,0.000525969,0.0001087262,0.0001195749,0.0002524792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9273117,"threshold_uncertainty_score":0.9996781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06895939526056154,"score_gpt":0.3055090934869426,"score_spread":0.236549698226381,"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."}}