{"id":"W2109754205","doi":"10.1109/tpwrd.2010.2046652","title":"Configuration Optimization of Underground Cables for Best Ampacity","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Thermal Analysis in Power Transmission","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Ampacity; Casing; Genetic algorithm; Mathematical optimization; Engineering; Optimization problem; Computer science; Mathematics; Electrical engineering; Electrical conductor; Mechanical engineering","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":[0.0003172858,0.001316256,0.0008759701,0.0007496957,0.0005386177,0.0007127489,0.0007389055,0.0006177409,0.00272998],"category_scores_gemma":[0.001490744,0.0005550425,0.0004125395,0.0006721754,0.0005058491,0.0007808082,0.0005921493,0.0005170178,0.0005989692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006921313,"about_ca_system_score_gemma":0.0008238468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001773033,"about_ca_topic_score_gemma":0.002574714,"domain_scores_codex":[0.9996368,0.0001239012,0.00001553128,0.00006864579,0.00009358861,0.00006168905],"domain_scores_gemma":[0.9995403,0.0001782016,0.0001063024,0.00004967382,0.00009025065,0.00003530196],"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":[0.00007347462,0.00002191355,0.0006063168,0.00004957641,0.00001539991,0.0001060297,0.00004986176,0.9535913,0.00575831,0.002495168,0.0006905479,0.03654207],"study_design_scores_gemma":[0.00003292155,0.000124395,0.0004893207,0.00001879954,0.00002065927,0.0001208696,0.00007055023,0.9869815,0.007032499,0.003411269,0.001675445,0.00002181433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1292172,0.0002412067,0.8624771,0.0001442424,0.0000246132,0.00007449694,0.0001259902,0.0006554415,0.007039735],"genre_scores_gemma":[0.7995236,0.0001654797,0.1971316,0.00002712884,0.00001594418,0.0001342919,0.0001777158,0.0001953783,0.002628875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00272998,"threshold_uncertainty_score":0.009132683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0106862358945746,"score_gpt":0.2180858467508955,"score_spread":0.2073996108563209,"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."}}