{"id":"W2112912287","doi":"10.1109/pesgm.2014.6938802","title":"Identification of umbrella constraints in DC-based security-constrained optimal power flow","year":2014,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Power flow; Constraint (computer-aided design); Computer science; Set (abstract data type); Scope (computer science); Power (physics); Mathematics; Electric power system","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":[0.0003952443,0.0001482892,0.0001986938,0.0001356183,0.00001613601,0.00002392034,0.0001338831,0.0001186216,0.0004182327],"category_scores_gemma":[0.00009264734,0.0001662044,0.0000649107,0.0002255235,0.000154983,0.0001132232,0.0000121902,0.0001388142,0.00008187164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006661925,"about_ca_system_score_gemma":0.00002646388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005960861,"about_ca_topic_score_gemma":0.000009924307,"domain_scores_codex":[0.9989116,0.00003643598,0.0004653928,0.0001835918,0.0001657291,0.000237316],"domain_scores_gemma":[0.9995073,0.00006599024,0.00004886049,0.0002450865,0.0000689574,0.00006382303],"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":[0.00005936424,0.0003375017,0.002731786,0.0003749478,0.00007092863,0.000009301875,0.0004904093,0.6194556,0.348492,0.01766474,0.001905806,0.008407672],"study_design_scores_gemma":[0.0008758155,0.00005087953,0.002733076,0.00003648089,0.00000975911,0.000003292641,0.00007370081,0.8899902,0.1055894,0.0001839857,0.0002333477,0.0002201697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7091963,0.00001943512,0.2756285,0.0000754383,0.0002974599,0.000218074,0.0001019394,0.0002318342,0.01423103],"genre_scores_gemma":[0.9972913,0.000001546682,0.002551891,0.00002017947,0.0000155189,0.000009330547,0.00007555694,0.00001879969,0.00001585675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2880951,"threshold_uncertainty_score":0.6777617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004870985073457442,"score_gpt":0.2122906967133333,"score_spread":0.2074197116398758,"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."}}