{"id":"W4238103177","doi":"10.32920/ryerson.14653575","title":"Modeling of the Lightning Return Stroke Current at a Tall Structure Using the Derivative of the Heidler Function","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Lightning and Electromagnetic Phenomena","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; ASTER","funders":"","keywords":"Waveform; Electric power transmission; Electrical impedance; Tower; Lossless compression; Derivative (finance); Reflection (computer programming); Lightning (connector); Reflection coefficient; Discontinuity (linguistics); Mathematical analysis; Acoustics; Series (stratigraphy); Physics; Computational physics; Mathematics; Electrical engineering; Computer science; Optics; Engineering; Geology; Algorithm; Structural engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001414778,0.0002911779,0.0003503912,0.00002636134,0.0003077344,0.00005809249,0.0005583611,0.00008943748,0.000208236],"category_scores_gemma":[0.000009741871,0.0001336837,0.0003678205,0.0001733952,0.00009587126,0.00004505511,0.001428419,0.0008696389,1.958173e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008268566,"about_ca_system_score_gemma":0.0002268025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004019845,"about_ca_topic_score_gemma":0.00003527605,"domain_scores_codex":[0.9983786,0.0001899337,0.0004363657,0.0003546047,0.0003773652,0.0002631321],"domain_scores_gemma":[0.9984004,0.0000485731,0.0004701793,0.0008387575,0.000212468,0.00002956509],"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":[0.00008022004,0.0002094563,0.02939053,0.0003687343,0.00163804,1.593868e-7,0.01909064,0.6806472,0.2567637,0.00759009,0.0002273386,0.003993907],"study_design_scores_gemma":[0.0008114369,0.00009042932,0.003166368,0.001662308,0.001105052,0.000003324017,0.005738367,0.7719517,0.1917401,0.02246619,0.0004497286,0.0008149814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841303,0.0006448622,0.01161519,0.0001808464,0.000969531,0.0003582565,0.00005986692,0.00001005211,0.002031113],"genre_scores_gemma":[0.9989162,0.000004687911,0.0003171245,0.00002570127,0.0003347748,0.00001136972,0.00002317826,0.00002462582,0.0003423225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09130449,"threshold_uncertainty_score":0.5451464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01876565735001624,"score_gpt":0.234838590750924,"score_spread":0.2160729334009078,"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."}}