{"id":"W3020790015","doi":"10.2316/j.2020.203-0214","title":"A PLANNING STRATEGY FOR REACTIVE POWER IN POWER TRANSMISSION NETWORK USING SOFT COMPUTING TECHNIQUES","year":2020,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Smart Grid and Power Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Power network; Power (physics); AC power; Transmission network; Power transmission; Transmission (telecommunications); Soft power; Computer science; Electric power system; Soft computing; Electrical engineering; Engineering; Telecommunications; Artificial neural network; Artificial intelligence; Physics","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":[0.0003245531,0.0001743492,0.0003394384,0.000150414,0.00003622457,0.0001099156,0.0001958234,0.0001101381,0.00000718962],"category_scores_gemma":[0.00001611698,0.0001552251,0.0001044906,0.0001035928,0.00001554965,0.0002442931,0.00001887975,0.0001861428,3.038342e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000839932,"about_ca_system_score_gemma":0.00003720515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004683637,"about_ca_topic_score_gemma":0.00000120944,"domain_scores_codex":[0.9986807,0.00004776158,0.0006487795,0.0001352638,0.0002741281,0.0002133786],"domain_scores_gemma":[0.9993305,0.0001136934,0.0001950573,0.00004907228,0.000183328,0.0001283939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004598367,0.00004848571,0.004870467,0.0001056253,0.0006247245,0.0003065823,0.007991765,0.9548851,0.01426768,0.001443208,0.01226506,0.002731471],"study_design_scores_gemma":[0.002679397,0.0008935528,0.001538477,0.004319444,0.00005368431,0.001495966,0.003948957,0.4464959,0.002615771,0.0001240855,0.53491,0.0009247893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1458289,0.007529302,0.8267012,0.0001890274,0.0144821,0.0001652195,0.0000199573,0.0001206881,0.004963681],"genre_scores_gemma":[0.9969876,0.00002510315,0.0006518106,0.00008571488,0.002197426,0.000002491394,0.000003226292,0.00003306917,0.00001359657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8511587,"threshold_uncertainty_score":0.6329896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02196300497391839,"score_gpt":0.2673665883311654,"score_spread":0.245403583357247,"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."}}