CN Tower Lightning Current Derivative Heidler Model for the Validation of Wavelet De-Noising Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Lightning current data collected at the CN Tower during the past 15 years need to be de-noised for precise analysis and accurate determination of the lightning return-stroke current waveform parameters. A wavelet transform algorithm has been developed for de- noising the lightning return-stroke current derivative signals measured at the CN Tower. This paper deals with the validation of the process of signal de-noising by the use of a Heidler modeled current derivative waveform. The process of the generation of a noised Heidler function will be discussed, and the results of the application of the wavelet de-noising algorithm will be presented. Different ranges of Heidler current derivative models have been generated and mixed with different noise only signals collected at the CN Tower. The application of the denoising algorithm on these noised signals has shown improvements of the SNR to up to 60 dB and correlation coefficients of up to 98 % between the Heidler models and their de-noised waveforms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it