{"id":"W4322628559","doi":"10.5281/zenodo.7685908","title":"A Branch-and-Cut Benders Decomposition Algorithm for Transmission Expansion Planning","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Power Systems and Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Benders' decomposition; Algorithm; Decomposition; Transmission (telecommunications); Mathematical optimization; Computer science; Mathematics; Chemistry; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0001935403,0.0001074897,0.0001194772,0.0001557891,0.0004899563,0.0002040245,0.0002433035,0.00007909607,0.0004111269],"category_scores_gemma":[0.00002035274,0.0001096197,0.00003413743,0.0001517776,0.00002941402,0.0001918765,0.0001158309,0.0001313222,0.0003602188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005774565,"about_ca_system_score_gemma":8.699258e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002752384,"about_ca_topic_score_gemma":3.41226e-8,"domain_scores_codex":[0.9992465,0.0000319463,0.0001491306,0.0002121715,0.0001315854,0.0002286008],"domain_scores_gemma":[0.9996319,0.00001688923,0.00002665364,0.0001779531,0.00007945157,0.00006717986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002175924,0.00002596781,0.000003449823,0.0002091132,0.00004071687,0.000003121044,0.001061829,0.0008118352,0.04785654,0.0007227935,0.01783986,0.931403],"study_design_scores_gemma":[0.000865122,0.0002669572,0.0002845053,0.0001573081,0.00001170917,0.00007956663,0.0004799324,0.07161319,0.008846294,0.0003353046,0.9168141,0.0002459774],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.144902,0.0007366723,0.8261915,0.0002277975,0.0003053217,0.001046296,0.0001204865,0.003872047,0.02259782],"genre_scores_gemma":[0.9962288,0.00008148674,0.002474805,0.00001852672,0.0000446905,1.215747e-7,0.0003587579,0.0006504934,0.0001423773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9311571,"threshold_uncertainty_score":0.4630004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01858139920632593,"score_gpt":0.2336138263609751,"score_spread":0.2150324271546492,"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."}}