{"id":"W2109247805","doi":"10.1109/tvcg.2008.197","title":"A Novel Visualization Technique for Electric Power Grid Analytics","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Electricity Delivery and Energy Reliability; U.S. Department of Energy","keywords":"Visualization; Blackout; Computer science; Usability; Electricity; Data visualization; Grid; Electric power; Electric power industry; Geographic information system; Electric power system; Data science; Strengths and weaknesses; Information visualization; Visual analytics; Systems engineering; Human–computer interaction; Data mining; Power (physics); Electrical engineering; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002803831,0.0003214749,0.0003029052,0.0009534956,0.0006845258,0.000220391,0.0004399257,0.0001964011,0.00001082049],"category_scores_gemma":[0.00001123218,0.0003343214,0.0001639473,0.002253743,0.00008982162,0.0006313847,0.000009423609,0.0001586775,0.000006335931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004463919,"about_ca_system_score_gemma":0.0001177246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007698781,"about_ca_topic_score_gemma":0.000005523085,"domain_scores_codex":[0.9979697,0.00008074551,0.0005320334,0.0006399049,0.0004315208,0.0003461116],"domain_scores_gemma":[0.9985494,0.0001390343,0.0001946023,0.000471059,0.0004544968,0.0001914192],"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":[0.00002657145,0.0008773145,0.00005380588,0.00005872591,0.00009684679,0.000004319976,0.0005773949,0.001693803,0.001001064,0.9924669,0.002219403,0.0009238658],"study_design_scores_gemma":[0.0008532159,0.000477221,0.0001217003,0.00003603246,0.00003612616,0.00009845715,0.00001085758,0.9824973,0.01060601,0.0005036459,0.004337747,0.0004216928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000524427,0.00002413076,0.997701,0.00005867087,0.0005933879,0.0006059466,0.00004807313,0.0004074818,0.00003684563],"genre_scores_gemma":[0.9723432,0.0009550109,0.02120812,0.004596706,0.0001821753,0.0001977673,0.0001410312,0.00009200166,0.0002839883],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9919633,"threshold_uncertainty_score":0.9999109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0289921746549816,"score_gpt":0.2933112848538452,"score_spread":0.2643191101988636,"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."}}