{"id":"W2581295120","doi":"10.1049/iet-gtd.2016.0923","title":"Multi‐group particle swarm optimisation for transmission expansion planning solution based on LU decomposition","year":2017,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Electric Power System Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Particle swarm optimization; Decomposition; Group (periodic table); Mathematical optimization; Transmission (telecommunications); Computer science; Decomposition method (queueing theory); Mathematics; Physics; Chemistry; Telecommunications; Statistics; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006226559,0.0004043945,0.0003074419,0.0001190268,0.001534433,0.0003524552,0.0002512957,0.0003671842,0.00003234263],"category_scores_gemma":[0.00005976551,0.0004172731,0.0001981601,0.0001726143,0.00004077869,0.000846764,0.000007900043,0.0002104638,0.00001331198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000456478,"about_ca_system_score_gemma":0.00005912814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001434626,"about_ca_topic_score_gemma":0.000004560573,"domain_scores_codex":[0.9976123,0.000134533,0.0006910143,0.0005696902,0.000487197,0.0005052684],"domain_scores_gemma":[0.9986819,0.00007207468,0.0002416061,0.0005514681,0.0002088933,0.000244077],"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":[0.0001770941,0.0001635573,0.00006130267,0.00009193391,0.00001474573,0.000001481521,0.000127798,0.5587173,0.3943121,0.00008013802,0.001681822,0.04457076],"study_design_scores_gemma":[0.002119653,0.0002265984,0.001117639,0.0002008072,0.0000531892,0.000002677735,0.00001335152,0.7176296,0.2755328,0.00002157612,0.002740637,0.0003415415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05661778,0.0002523563,0.9399077,0.0005781717,0.0006955548,0.001195238,0.0001121217,0.0005539196,0.00008716041],"genre_scores_gemma":[0.9626545,0.00008579429,0.03100499,0.00005680577,0.0002744622,0.0003224841,0.005479526,0.00007389806,0.00004755578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9089027,"threshold_uncertainty_score":0.9998279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03010699611700365,"score_gpt":0.2837620201590405,"score_spread":0.2536550240420369,"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."}}