A performance assessment of the World Wide Lightning Location Network (WWLLN) via comparison with the Canadian Lightning Detection Network (CLDN)
Bibliographic record
Abstract
Abstract. The World Wide Lightning Location Network (WWLLN) uses globally-distributed Very Low Frequency (VLF) receivers in order to observe lightning around the globe. Its objective is to locate as many global lightning strokes as possible, with high temporal and spatial (< 10 km) accuracy. Since detection is done in the VLF range, signals from high peak current lightning strokes are able to propagate up to ~104 km before being detected by the WWLLN sensors, allowing for receiving stations to be sparsely spaced. Through a comparison with measurements made by the Canadian Lightning Detection Network (CLDN) between May and August 2008 over a 4° latitude by 4° longitude region centered on Toronto, Canada, this study found that WWLLN detection was most sensitive to high peak current lightning strokes. Events were considered shared between the two networks if they fell within 0.5 ms of each other. Using this criterion, 19 128 WWLLN strokes (analyzed using the Stroke_B algorithm) were shared with CLDN lightning strokes, producing a detection efficiency of 2.8%. The peak current threshold for WWLLN detection is found to be ~20 kA, with its detection efficiency increasing from 11.3% for peak currents greater than 20 kA to 75.8% for peak currents greater than 120 kA. The detection efficiency is seen to have a clear diurnal dependence, with a higher detection efficiency at local midnight than at local noon; this is attributed to the difference in the thickness of the ionospheric D-region between night and day. The mean time difference (WWLLN − CLDN) between shared events was −6.44 μs with a standard deviation of 35 μs, and the mean absolute location accuracy was 7.24 km with a standard deviation of 6.34 km. These results are generally consistent with previous comparison studies of the WWLLN with other regional networks around the world. Additional receiver stations are continuously being added to the network, acting to improve this detection efficiency.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".