Evaluation of the performance characteristics of the North American Lightning Detection Network based on recent CN Tower lightning data
Bibliographic record
Abstract
Using the CN Tower lightning data acquired on September 5, 2014, the performance characteristics of the North American Lightning Detection Network (NALDN) are evaluated. The evaluation includes polarity, stroke detection efficiency, location accuracy and peak current estimation. On that day, the tower was struck with 24 flashes, based on video records. However, only nine of these flashes were found to contain return strokes based on current records and luminosity analysis of video records. These nine flashes contained a total of 30 return strokes. The NALDN detected all these return strokes, resulting in a perfect return-stroke detection efficiency. All recorded return strokes were proven to be negative, complying with NALDN prediction. Relative to the tower, for the 30 detected strokes, the NALDN was found to have a median absolute location error of 124.7m and an average absolute location error of 136.77m, each is about one-third of that determined based on a 2005 network evaluation. It was also demonstrated that the NALDN stroke location error have a substantial bias towards the north of the tower and a marked bias towards the west. The NALDN is found to overestimate the current peak measured at the tower, which is due to the higher speed of propagation within the tall tower, approximately at the speed of light in free space, in comparison with the speed of propagation of return strokes. The presented work shows that the NALDN upgrades, beyond 2005, have substantially improved the NALDN performance characteristics, especially in terms of stroke-detection efficiency and location accuracy.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".