{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008317014,0.0005589342,0.0006062895,0.000308744,0.0005121934,0.0006155035,0.001113186,0.000433714,0.0003858568],"category_scores_gemma":[0.001870856,0.0002591612,0.0003176184,0.0004303923,0.0007751549,0.0008801339,0.000941277,0.0005093453,0.00005077325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320511,"about_ca_system_score_gemma":0.001238288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01211338,"about_ca_topic_score_gemma":0.008408034,"domain_scores_codex":[0.9994724,0.0001534263,0.00001960506,0.00009974574,0.0001049008,0.0001498435],"domain_scores_gemma":[0.9992817,0.0003235295,0.0001313393,0.00005795223,0.0001180326,0.00008733394],"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":[0.00005805229,0.00001428863,0.0004761057,0.00002367079,0.0000184965,0.00007386325,0.00005004577,0.9762364,0.002441995,0.008155331,0.0002356314,0.0122161],"study_design_scores_gemma":[0.000002645019,0.00001885843,0.00007845806,0.000001715277,0.000004842351,0.00001269235,0.00001193364,0.9968193,0.0004469675,0.002469021,0.0001298717,0.000003759936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08196334,0.0005864779,0.9155862,0.0001231808,0.00003468325,0.0000274571,0.00003532619,0.0001253736,0.001517918],"genre_scores_gemma":[0.9866947,0.0001340189,0.01283762,0.00002187455,0.000009125871,0.00001607642,0.00001631475,0.000005728662,0.0002644614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01211338,"threshold_uncertainty_score":0.02408576,"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."}}