{"id":"W4404132515","doi":"10.1109/jiot.2024.3493613","title":"Cooperative Resource Scheduling for Environment Sensing in Satellite–Terrestrial Vehicular Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Satellite Communication Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scheduling (production processes); Satellite; Resource management (computing); Communications satellite; Computer network; Distributed computing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001025356,0.0001712553,0.0002755813,0.0002099384,0.0000319077,0.0002131845,0.0002951222,0.0001271418,0.00001391799],"category_scores_gemma":[0.000046038,0.0001640054,0.0001423508,0.0001159624,0.00004277901,0.0002548194,0.00003627844,0.0006721605,0.0000115353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002479385,"about_ca_system_score_gemma":0.00001869293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001279166,"about_ca_topic_score_gemma":0.000002139873,"domain_scores_codex":[0.9985822,0.0001153887,0.0007114173,0.0001532959,0.0001949778,0.0002427299],"domain_scores_gemma":[0.9992692,0.0003293227,0.000094693,0.0002094996,0.0000263631,0.00007096265],"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":[0.00009001993,0.00001771377,0.0001001338,0.0001238781,0.0002449383,0.00006690067,0.008073342,0.9223124,0.01712381,0.0000676248,0.0004561276,0.05132314],"study_design_scores_gemma":[0.0003930433,0.00004930585,0.00001662089,0.001368814,0.00001954407,0.0001827278,0.0002983135,0.9321218,0.01222745,0.00007989325,0.05307457,0.000167985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4622316,0.03904033,0.4937594,0.0002026207,0.003320431,0.000458635,0.00000267781,0.0001667469,0.0008175211],"genre_scores_gemma":[0.991521,0.001179867,0.006612615,0.00003585191,0.0004993767,0.00000458378,0.000004649708,0.00005471315,0.00008731974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5292894,"threshold_uncertainty_score":0.6687946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02394949633618184,"score_gpt":0.2504720807260675,"score_spread":0.2265225843898856,"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."}}