{"id":"W2324451540","doi":"10.2316/journal.203.2014.3.203-0055","title":"A NOVEL LINE SELECTION METHOD FOR POWER DISTRIBUTION NETWORKS WITH INDIRECTLY EARTHED NEUTRAL","year":2014,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Electromagnetic Compatibility and Noise Suppression","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Line (geometry); Power (physics); Distribution (mathematics); Electrical engineering; Biological system; Computer science; Physics; Biology; Mathematics; Engineering; Mathematical analysis; Artificial intelligence; Thermodynamics; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002553054,0.000596818,0.0005699726,0.000650726,0.0004652129,0.0005395694,0.000750492,0.0003789232,0.00466878],"category_scores_gemma":[0.0006424097,0.0002012489,0.0003582981,0.0006274565,0.0001889543,0.0005113671,0.000341141,0.000380573,0.0010709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002219662,"about_ca_system_score_gemma":0.0004902227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001793294,"about_ca_topic_score_gemma":0.003520047,"domain_scores_codex":[0.9997484,0.00006036695,0.0000118504,0.00004385494,0.0001134853,0.00002205272],"domain_scores_gemma":[0.9997099,0.00009543774,0.00002753639,0.00002782285,0.0001183348,0.00002092905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003506598,0.0001983669,0.001211771,0.0001981914,0.00007695705,0.0002703646,0.0001383303,0.08507311,0.07356539,0.009997924,0.008667744,0.8202513],"study_design_scores_gemma":[0.00005272607,0.0000953202,0.0006341999,0.00001171298,0.00003840409,0.0002793975,0.0000411255,0.9807879,0.009357211,0.002481381,0.006198698,0.00002190178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008536329,0.0001400835,0.9887549,0.00004332054,0.00005062129,0.00004181439,0.00003033079,0.00044345,0.001959225],"genre_scores_gemma":[0.2597935,0.000305054,0.7288808,0.00009308719,0.0001561997,0.0001418498,0.0002445628,0.000202408,0.01018251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00466878,"threshold_uncertainty_score":0.01561862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004648345278502515,"score_gpt":0.2212339533703254,"score_spread":0.2165856080918229,"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."}}