{"id":"W4388004989","doi":"10.1145/3616394.3618275","title":"Are ML Models Scenario-Independent in Enhancing Routing Efficiency for Smart Grid Networks?","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Computer science; Smart grid; Context (archaeology); Grid; Quality of service; Routing protocol; Routing (electronic design automation); Machine learning; Data mining; Distributed computing; Artificial intelligence; Computer network; Engineering","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":[0.003369807,0.001045556,0.0006935466,0.0006801066,0.0003262979,0.001494287,0.001279312,0.001291019,0.001337734],"category_scores_gemma":[0.01495854,0.000459168,0.0006052029,0.0006162842,0.0006422722,0.005160597,0.0009357464,0.001645425,0.0007961995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007391048,"about_ca_system_score_gemma":0.000942532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002860508,"about_ca_topic_score_gemma":0.005075852,"domain_scores_codex":[0.9989059,0.0005787525,0.00006272909,0.0002373328,0.0001271302,0.0000880397],"domain_scores_gemma":[0.9947152,0.003499122,0.0005110204,0.0006124215,0.0005337626,0.0001284701],"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":[0.0002417986,0.0001316694,0.007836042,0.0001476752,0.0001755271,0.00009622973,0.0001025127,0.8876394,0.002471728,0.005949356,0.002213921,0.09299402],"study_design_scores_gemma":[0.00001157709,0.00007875189,0.001215194,0.00002405428,0.00002732156,0.00006589123,0.00006297598,0.9877703,0.001101214,0.008883401,0.000742385,0.00001697366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.25112,0.00242693,0.7275354,0.006961164,0.0004473019,0.0001512212,0.001117922,0.00219105,0.008048905],"genre_scores_gemma":[0.9398708,0.0007114256,0.05680654,0.0005122676,0.0001455697,0.00008635008,0.0006642911,0.0001141426,0.001088746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003369807,"threshold_uncertainty_score":0.01782149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817424074188245,"score_gpt":0.2267458934994102,"score_spread":0.2085716527575278,"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."}}