CN Tower Lightning Current Derivative Heidler Model for the Validation of Wavelet De-Noising Algorithm
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".