{"id":"W2157128070","doi":"10.1109/infcomw.2011.5928934","title":"k-robust network design using resistance distance: Case of RocketFuel and power grids","year":2011,"lang":"en","type":"article","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Network topology; Computer science; Mathematical optimization; Robust optimization; Optimization problem; Network planning and design; Linear programming; Grid; Interior point method; Topology optimization; Topology (electrical circuits); Distributed computing; Mathematics; Computer network; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004713187,0.0001155429,0.0001865026,0.00003875521,0.0001493543,0.0000553196,0.0002240829,0.00006091108,0.00004206509],"category_scores_gemma":[0.00001067054,0.00009560336,0.00004083562,0.0002766922,0.00004952014,0.0003074023,0.00009453372,0.00007192547,0.000002156888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000222553,"about_ca_system_score_gemma":0.00002483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001319392,"about_ca_topic_score_gemma":0.0002277296,"domain_scores_codex":[0.9989667,0.000110107,0.0002986835,0.0002849384,0.00009997317,0.0002395887],"domain_scores_gemma":[0.9992427,0.00007479994,0.000124865,0.0003733142,0.0001110437,0.00007325027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002295063,0.0001929583,0.003273887,0.0001612234,0.0001492142,0.001777491,0.01031252,0.02530676,0.0002719053,0.9138177,0.04136559,0.003141267],"study_design_scores_gemma":[0.0009759407,0.0003578833,0.000840821,0.0004519858,0.00003344129,0.003300162,0.001036695,0.9675784,0.001382524,0.01762257,0.005458175,0.0009614279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.010225,0.0005211663,0.9795067,0.00001422273,0.0008499199,0.0001247448,6.950311e-7,0.00005533984,0.008702242],"genre_scores_gemma":[0.8400328,0.000007892377,0.1587742,0.00006126038,0.00006840814,0.000003166273,7.862378e-8,0.000006933855,0.001045245],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9422716,"threshold_uncertainty_score":0.3898592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08063400341921646,"score_gpt":0.2395271197567233,"score_spread":0.1588931163375069,"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."}}