Recovery of valuable incompletely recorded CN tower lightning return stroke current derivative signals
Bibliographic record
Abstract
Lightning is a captivating natural phenomenon but indisputably terrifying. Therefore, lightning studies have played an essential role in establishing safety regulations to protect lives and infrastructures. Among the many simulating functions that were utilized in the past for modelling the lightning return-stroke current, Heidler and Pulse functions overcame certain limitations, including the time derivative discontinuities. Incompletely-recorded current derivative signals represent another challenge in lightning research. This thesis proposes a double-term Pulse function that could be investigated with the double-term Heidler function for modelling the lightning return-stroke current. The time derivative of both Pulse and Heidler functions have been used to simulate the current derivative signals recorded on June 10, 1996. Some of these return-stroke signals exceded the maximum set level. Consequently, the double-term simulating functions were used to recover a large incompletely recorded return-stroke current derivative signal. The R2 fitting factor was used to evaluate the quality of each fitting to determine which simulating function is better suited to model and recover valuable return-stroke current signals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".