Evaluation of the Performance Characteristics of the North American Lightning Detection Network Based on Tall-Structure Lightning
Bibliographic record
Abstract
Using Canadian National (CN) Tower lightning data acquired during the summer of 2005, the performance characteristics of the North American Lightning Detection Network (NALDN) were evaluated, including the flash detection efficiency, stroke detection efficiency, absolute location error, location accuracy model error (50%, 90%, and 99% error ellipses), and peak current estimation. The NALDN detected seven out of the seven flashes recorded at the CN tower, resulting in a 100% flash detection efficiency. Furthermore, the NALDN detected 21 out of the 38 return strokes recorded at the tower, resulting in a stroke detection efficiency of 55%. Relative to the CN tower, the NALDN was found to have a median and mean absolute stroke location error of 0.358 and 0.395 km, respectively, for the 21 detected strokes. It was also demonstrated that the NALDN stroke location error seems to have a clear bias towards the north of the CN tower and a slight bias toward the east, with 18 of the 21 strokes predicted to be northeast of the tower. The 50%, 90%, and 99% error ellipses provided by the NALDN were also evaluated. It was found that 71% of the detected strokes (15 out of 21) were enclosed by the 50% error ellipse, 90% of the detected strokes (19 out of 21) were enclosed by the 90% error ellipse, and 95% of the detected strokes (20 out of 21) were enclosed by the 99% error ellipse. The minimum value for the 50% error ellipse axis is set at 0.4 km by Vaisala, Inc., and 20 of the 21 detected strokes had a semimajor axis length of 0.4 km, suggesting that the median location error for strokes hitting the CN tower is 0.4 km or less. The 0.358 km median location error obtained for the 21 detected strokes appears to support this. The dependence of stroke detection efficiency and location error on the characteristics of the current measured at the CN tower is evaluated. The NALDN is found to overestimate the current peak, which is possible to explain for tall-structure lightning. The dependence of stroke detection efficiency and location error on the characteristics of the CN tower lightning-generated electromagnetic pulse, measured 2 km north of the tower, is also evaluated.
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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 | 0.003 | 0.007 |
| 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) | 0.001 | 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".