{"id":"W2968560091","doi":"10.22098/joape.2019.5522.1414","title":"FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems","year":2020,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Optimization algorithm; Mathematical optimization; Computer science; Engineering optimization; Power optimization; Power (physics); Optimization problem; Algorithm; Mathematics; Power consumption; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001647595,0.0004109123,0.0007806118,0.0008449531,0.0003571233,0.003649024,0.004720243,0.0001520787,0.001728541],"category_scores_gemma":[0.001651996,0.000420942,0.0002877846,0.002654084,0.0000460761,0.005587969,0.00167909,0.0003865137,0.00000920343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319931,"about_ca_system_score_gemma":0.0002683381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003299787,"about_ca_topic_score_gemma":4.3458e-7,"domain_scores_codex":[0.995943,0.0001604161,0.001169172,0.0008578756,0.001235091,0.0006344891],"domain_scores_gemma":[0.9967316,0.0004826863,0.0007406714,0.0006091495,0.0008950209,0.0005408846],"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":[0.00001377037,0.00009990414,0.0005692322,0.00006937253,0.0001498389,0.0000156382,0.0002743375,0.9804124,0.00161198,0.0001397653,0.00406936,0.01257436],"study_design_scores_gemma":[0.0007797272,0.00002972041,0.0004169747,0.0001615514,0.00003763365,0.000006653481,0.00001753822,0.9944966,0.002088909,0.0001897253,0.001312531,0.0004624391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001721513,0.002646457,0.9935973,0.0006272545,0.0008740225,0.001377571,0.00002713769,0.0001957192,0.0004823297],"genre_scores_gemma":[0.01820589,0.001565338,0.9791159,0.0004075365,0.0002201486,0.0001813601,0.00004822814,0.0001146823,0.0001409136],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01803374,"threshold_uncertainty_score":0.9998242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1893431910617587,"score_gpt":0.4907672931665305,"score_spread":0.3014241021047718,"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."}}