{"id":"W4415048448","doi":"10.1109/tsc.2025.3620092","title":"ERAP Optimization via Enhanced Constraints and Boundary Detection in GMRA","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Enhanced Data Rates for GSM Evolution; Edge computing; Server; Resource allocation; Asynchronous communication; Workload; Resource management (computing); Throughput; Acceleration","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":[0.001619998,0.001532087,0.001362225,0.0007025272,0.0005171482,0.001424194,0.001908708,0.0013603,0.003988893],"category_scores_gemma":[0.004960487,0.0006953421,0.0009468163,0.0008056559,0.0008514614,0.001931718,0.001883015,0.001786389,0.0005584323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007693959,"about_ca_system_score_gemma":0.001837243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006100892,"about_ca_topic_score_gemma":0.004496554,"domain_scores_codex":[0.9985878,0.0005145971,0.00007214939,0.0002880792,0.0003047901,0.0002325988],"domain_scores_gemma":[0.9979576,0.00123031,0.0002330528,0.0001686089,0.0002913914,0.0001190959],"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":[0.00008160487,0.0000517096,0.0003376024,0.00008313687,0.00002067724,0.0001090203,0.00004891399,0.962334,0.002577603,0.009535869,0.001027578,0.02379224],"study_design_scores_gemma":[0.000007970621,0.00002086285,0.00003831641,0.000004571406,0.00000290897,0.0000122626,0.000007386278,0.9977508,0.0003940554,0.001481517,0.0002753375,0.000003965726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01905031,0.0002265768,0.9769047,0.0001939212,0.00004549393,0.00008023675,0.00006797698,0.0003339948,0.003096694],"genre_scores_gemma":[0.5694851,0.0002398782,0.4250571,0.0002744913,0.00005716692,0.0003529642,0.0002443494,0.0002133577,0.004075543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006100892,"threshold_uncertainty_score":0.01334417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003415647329898937,"score_gpt":0.2084368609081118,"score_spread":0.2050212135782128,"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."}}