{"id":"W2322542903","doi":"10.2316/journal.203.2011.2.203-4835","title":"HEURISTIC JUSTIFICATION AND DIFFERENTIAL EVOLUTION-BASED FINAL SELF-RESTORATION STATE OPTIMIZATION FOR URBAN POWER GRID AFTER BLACKOUT","year":2011,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Power Systems and Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Blackout; Differential evolution; Heuristic; Grid; Mathematical optimization; Electric power system; Computer science; Power (physics); Power grid; Differential (mechanical device); State (computer science); Reliability engineering; Operations research; Engineering; Mathematics; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001536172,0.0001373479,0.0001727455,0.0002560947,0.00003643894,0.00009530796,0.0001265883,0.00008610333,0.00001533868],"category_scores_gemma":[0.00002652065,0.0001203772,0.00005284219,0.00004746942,0.00003362523,0.0002488527,0.00001450851,0.00007289262,6.962947e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001043232,"about_ca_system_score_gemma":0.00002875441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004887509,"about_ca_topic_score_gemma":0.00000757666,"domain_scores_codex":[0.9990715,0.0000293379,0.0004455801,0.0001157802,0.0002168457,0.0001209733],"domain_scores_gemma":[0.9992786,0.00003639712,0.0001938369,0.00008533391,0.0003466862,0.00005915747],"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":[0.004384261,0.001736768,0.07520045,0.001812643,0.006796367,0.0004549379,0.01770654,0.768459,0.009910605,0.07405336,0.0268896,0.0125955],"study_design_scores_gemma":[0.003884624,0.000859657,0.04370734,0.0006779425,0.0002061789,0.0003796567,0.000593271,0.927559,0.001333746,0.0006941423,0.01924435,0.0008600438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1800067,0.001361338,0.813328,0.00003139166,0.004840292,0.00007920432,0.00005278438,0.00009238442,0.0002079588],"genre_scores_gemma":[0.9986298,0.00006224995,0.00097627,0.000007225621,0.0001989921,0.00002569644,0.00001282031,0.00001931988,0.00006760545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8186231,"threshold_uncertainty_score":0.4908838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009204178229971854,"score_gpt":0.1919916464617682,"score_spread":0.1827874682317963,"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."}}