{"id":"W4415178829","doi":"10.1109/tnse.2025.3621342","title":"RobGenX: Uncertainty-Aware Inference in eXtreme Computing Power Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Qatar University","keywords":"Inference; Integer programming; Enhanced Data Rates for GSM Evolution; Edge computing; Probabilistic logic; Linear programming; Approximate inference; Task (project management); Cloud computing; Stochastic programming","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":[0.0004035725,0.0001921603,0.0001837123,0.0003267201,0.0002590238,0.00009913876,0.0002566779,0.00009428742,0.00000871363],"category_scores_gemma":[0.00001329032,0.0001979778,0.00003434047,0.002031265,0.0001357053,0.0002619678,0.00000516907,0.0004597063,0.000002789483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001315417,"about_ca_system_score_gemma":0.00006394489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001977905,"about_ca_topic_score_gemma":0.00007204692,"domain_scores_codex":[0.9986706,0.000009601493,0.0002336835,0.0002984267,0.0002068116,0.0005808407],"domain_scores_gemma":[0.999438,0.000201079,0.00001259887,0.0001975585,0.0000468622,0.0001038484],"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":[0.000003608415,0.00001084239,0.0001044843,0.0000233502,0.00000661244,0.000004925825,0.0001500104,0.9929501,0.000325463,0.0002078672,0.00005244118,0.0061603],"study_design_scores_gemma":[0.0001543748,0.00001735628,0.001618076,0.0002845425,0.000005305045,0.000003230565,0.000057806,0.9968877,0.0003103014,0.00002015381,0.0004393897,0.0002017215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1334072,0.0004603491,0.8631132,0.00003705261,0.002108955,0.0001317469,0.000001169584,0.0002574005,0.0004829222],"genre_scores_gemma":[0.9991204,0.0002747124,0.0004277225,0.00007820442,0.00005693041,0.00001145676,3.270058e-7,0.00001194154,0.0000183034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8657132,"threshold_uncertainty_score":0.80733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114634822332282,"score_gpt":0.2131595080223111,"score_spread":0.2016960257890829,"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."}}