{"id":"W4307442695","doi":"10.32920/21408594.v1","title":"Rectangular branch‐based load flow","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Jacobian matrix and determinant; Mathematics; Slack bus; Node (physics); Power (physics); Incidence matrix; Voltage; AC power; Flow (mathematics); Mathematical analysis; Control theory (sociology); Applied mathematics; Geometry; Power-flow study; Computer science; Physics; Electrical engineering; 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":[0.0003023671,0.0006140396,0.0006283332,0.0005128635,0.0003136568,0.0008888994,0.0008147105,0.0003825714,0.01693551],"category_scores_gemma":[0.0008425452,0.000366854,0.0004957726,0.0009006449,0.0003456876,0.001022744,0.000724996,0.0006621779,0.002724322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005015568,"about_ca_system_score_gemma":0.0008584355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005032474,"about_ca_topic_score_gemma":0.004533441,"domain_scores_codex":[0.999791,0.0000451358,0.00001167069,0.00006260834,0.0000662436,0.00002330826],"domain_scores_gemma":[0.9998034,0.00005806228,0.00002359224,0.00003439643,0.00006910827,0.00001147758],"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":[0.00011354,0.00004663185,0.0007914561,0.0001061646,0.00002104058,0.00006499883,0.0001186366,0.6912417,0.007954879,0.03535685,0.004708177,0.2594759],"study_design_scores_gemma":[0.00001191454,0.00002101831,0.00009094291,0.000005430384,0.000003487871,0.0000114619,0.00001145294,0.9896364,0.00110395,0.005687508,0.003411817,0.000004752329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007050477,0.00004945046,0.985799,0.00005841639,0.0000241591,0.00006474281,0.0001232993,0.0005206576,0.00630987],"genre_scores_gemma":[0.4136406,0.0002969926,0.5631114,0.00009590683,0.00005338778,0.0002857687,0.0009639457,0.0003795811,0.02117242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01693551,"threshold_uncertainty_score":0.05665487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009527761643143923,"score_gpt":0.2193289089075632,"score_spread":0.2098011472644193,"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."}}