{"id":"W4244163424","doi":"10.32920/ryerson.14654283.v1","title":"Optimal Resource Allocation For QOS Supports In Smart Grid Neighborhood Area Network","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Quality of service; Computer science; Smart grid; Computer network; Network packet; Packet loss; Latency (audio); Reliability (semiconductor); Wide area network; Grid; Telecommunications network; Distributed computing; Power (physics); Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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"],"consensus_categories":[],"category_scores_codex":[0.0003108255,0.0003141901,0.0003945096,0.0001009289,0.00005512718,0.0001129592,0.0003024896,0.0004565388,0.0001469425],"category_scores_gemma":[0.00005066685,0.000330586,0.0001629996,0.0001961471,0.00002916271,0.00008691358,0.0002508004,0.0006189127,0.000006718781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001090412,"about_ca_system_score_gemma":0.0001041134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000487554,"about_ca_topic_score_gemma":0.0004190877,"domain_scores_codex":[0.9983271,0.00003452659,0.0004483928,0.0004939946,0.0001978391,0.0004981944],"domain_scores_gemma":[0.9991417,0.000126408,0.00005409701,0.0005123907,0.00005799246,0.0001074256],"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":[0.00001338105,0.00003504775,0.001735366,0.0003068595,0.00004093538,0.00002270865,0.0004725036,0.9683722,0.00003888266,0.0001846958,0.02827383,0.0005036335],"study_design_scores_gemma":[0.0005908787,0.0000517263,0.007813452,0.0007231185,0.00006053162,0.0000260438,0.0006018417,0.9329737,0.001685371,0.0003158683,0.05410587,0.001051594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5944583,0.005951899,0.3380185,0.00129146,0.02087674,0.003855469,0.0001160621,0.00162101,0.03381051],"genre_scores_gemma":[0.9857536,0.0003814801,0.008958289,0.0001893905,0.002600494,0.0004125924,0.001198143,0.00008691439,0.0004190857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3912953,"threshold_uncertainty_score":0.9999146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118762225877551,"score_gpt":0.2158360791582146,"score_spread":0.2046484568994391,"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."}}