{"id":"W2123942073","doi":"10.1109/pesmg.2013.6672425","title":"Analytical calculation of leakage inductance for low-frequency transformer modeling","year":2013,"lang":"en","type":"article","venue":"","topic":"Magnetic Properties and Applications","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Leakage inductance; Finite element method; Inductance; Electromagnetic coil; Transformer; Computation; Magnetic flux leakage; Leakage (economics); Electronic engineering; Equivalent series inductance; Magnetic flux; Computer science; Engineering; Electrical engineering; Physics; Structural engineering; Algorithm; Magnetic field; Voltage","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009039071,0.00005434535,0.00009550456,0.00001808672,0.00004754207,0.00002022783,0.000103321,0.00003526775,0.001716085],"category_scores_gemma":[0.00001914201,0.00003911436,0.00003643249,0.00006140395,0.00003821795,0.0001414806,0.000005543326,0.00002234281,0.00006188457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009123056,"about_ca_system_score_gemma":0.00002092568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003779366,"about_ca_topic_score_gemma":0.0000120653,"domain_scores_codex":[0.999409,0.000005875566,0.000227034,0.0001332825,0.00009120172,0.0001335634],"domain_scores_gemma":[0.9996651,0.00001615042,0.00002680487,0.0001427947,0.0001116079,0.00003755774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003232838,0.00002876287,0.0000202547,0.0000332375,0.000001387941,1.369531e-8,0.0000827567,0.0006034197,0.9514239,0.04620544,0.0001591433,0.001438386],"study_design_scores_gemma":[0.0002692078,0.00006210301,0.0002087372,0.00001715109,0.00001507028,7.522626e-7,0.0001028122,0.7939738,0.1862089,0.01889382,0.0001115999,0.000136033],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.737868,0.00002237575,0.2557059,0.0005728949,0.00002220672,0.000339575,0.0000047637,0.00001895392,0.005445324],"genre_scores_gemma":[0.9713055,0.000003743162,0.02799548,0.00007002036,0.00002504145,0.0001270392,0.000003564858,0.000005402328,0.0004642375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7933704,"threshold_uncertainty_score":0.9991965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0292497784431059,"score_gpt":0.2580410281237409,"score_spread":0.228791249680635,"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."}}