{"id":"W4242273836","doi":"10.1109/tia.2016.2582827","title":"The Formulation of a Power Flow Using <inline-formula> <tex-math notation=\"LaTeX\">$d-q$</tex-math> </inline-formula> Reference Frame Components—Part I: Balanced <inline-formula> <tex-math notation=\"LaTeX\">$3\\phi$ </tex-math> </inline-formula> Systems","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Jacobian matrix and determinant; AC power; Mathematics; Convergence (economics); Control theory (sociology); Reference frame; Power (physics); Applied mathematics; Voltage; Engineering; Computer science; Frame (networking); Electrical engineering; 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","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.003249377,0.004739725,0.004202984,0.002432015,0.004456432,0.001449439,0.004086204,0.005235862,0.0004331605],"category_scores_gemma":[0.0005326036,0.004330888,0.00214295,0.006128909,0.00125297,0.005465859,0.0002678025,0.004925018,0.002243659],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004592818,"about_ca_system_score_gemma":0.0016556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001969298,"about_ca_topic_score_gemma":0.0006674911,"domain_scores_codex":[0.9760131,0.00072346,0.0087894,0.004397076,0.005055177,0.00502176],"domain_scores_gemma":[0.9807289,0.002384017,0.003753092,0.006524325,0.004434298,0.002175358],"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":[0.001255674,0.004402929,0.0002002952,0.001547485,0.00274316,0.00006760014,0.00214733,0.4876507,0.4079584,0.07325572,0.003300968,0.01546979],"study_design_scores_gemma":[0.008533071,0.0009752981,0.002305142,0.002639503,0.001925748,0.0004769853,0.000356763,0.8254843,0.01840675,0.001240691,0.1321317,0.005524085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5264371,0.001206066,0.443381,0.001351653,0.003718514,0.008225633,0.01109601,0.002914782,0.001669182],"genre_scores_gemma":[0.9716651,0.00149614,0.0115708,0.0002807459,0.001633386,0.004363783,0.005291101,0.001188394,0.002510559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.445228,"threshold_uncertainty_score":0.9995872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02453700293385918,"score_gpt":0.2560452410467817,"score_spread":0.2315082381129225,"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."}}