{"id":"W2551547551","doi":"10.1109/naps.2016.7747921","title":"Optimal partitioning of secondary grid networks to reduce load shed under second contingency","year":2016,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Grid; Contingency; Partition (number theory); Computer science; Distributed computing; Grid network; Representation (politics); Reliability engineering; Mathematical optimization; Engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002585346,0.0006459724,0.0006312967,0.0004414818,0.000361483,0.0004413085,0.0005008159,0.0003136081,0.001900804],"category_scores_gemma":[0.001156491,0.0002394066,0.0002573571,0.0003094825,0.0002993348,0.0005820711,0.0005677349,0.0003426967,0.000241657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005911768,"about_ca_system_score_gemma":0.000477837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002608894,"about_ca_topic_score_gemma":0.003941571,"domain_scores_codex":[0.9997824,0.00007747499,0.000006629919,0.00003881028,0.00004350958,0.00005114395],"domain_scores_gemma":[0.9996926,0.0001464675,0.00004173327,0.00003596961,0.00005472964,0.00002867118],"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":[0.0000972901,0.0000541472,0.0007244588,0.00004274231,0.00001380525,0.00007227567,0.00009477101,0.9349271,0.009344568,0.005523284,0.001315568,0.04778995],"study_design_scores_gemma":[0.000008914179,0.00004245322,0.0003116449,0.000005732284,0.000006740556,0.00004650203,0.00002771152,0.9922819,0.002183974,0.004060532,0.001019693,0.000004186959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09666032,0.0001988687,0.8965676,0.00009263305,0.00002815439,0.00006952155,0.00006934455,0.0002342335,0.006079267],"genre_scores_gemma":[0.9016276,0.0001263383,0.09657957,0.00002770609,0.00001501718,0.00006057,0.00009850297,0.00005682492,0.001407887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002608894,"threshold_uncertainty_score":0.006358802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007564281909062538,"score_gpt":0.2150471725807166,"score_spread":0.2074828906716541,"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."}}