Cloud-to-Ground Lightning in Canada: 20 Years of CLDN Data
Bibliographic record
Abstract
This study presents the spatial and temporal features of more than 45 million cloud-to-ground (CG) lightning flashes recorded by the Canadian Lightning Detection Network for the years 1999–2018. Although sensor upgrades have improved the detection efficiency and location accuracy of CG lightning, the large-scale spatial patterns remain about the same as found in a previous study covering the years 1999–2008. Analyses, using equal-area squares with 10 km sides, describe the regional and seasonal characteristics of negative and positive flashes, the percentage and flash density of positive lightning, and the first-stroke peak currents of both polarities. Lightning activity over the provinces and territories is greatest in the summer, varying from 95.9% to 76.8% of the annual activity in the Northwest Territories and Ontario, respectively. Winter lightning is rare, usually occurring in extreme southern Ontario and the Atlantic Provinces, as well as over offshore regions west of Vancouver Island and the coastal waters off Nova Scotia. Preliminary analysis suggests that, compared with the 1999–2008 period, the majority of western and northern Canada has experienced more lightning days during the 2009–2018 period, whereas much of eastern Canada has experienced fewer lightning days. A statistical analysis performed on 154 stations across Canada found that the decadal increases (decreases) at 5 (31) stations were significant at the 90% confidence level or higher, and 4 (16) of these were significant at the 95% confidence level.
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 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".