Cloud-to-Ground Lightning in Canada: 1999 - 2008
Bibliographic record
Abstract
Cloud-to-ground (CG) lightning is a major cause of severe weather-related fatalities and injuries (Mills et al. 2009; Curran et al. 2000), property and infrastructure damage (Holle et al. 1996; Mills et al., 2008), forest fires (Stocks et al., 2002), and interruptions in or damage to electric power transmission and distribution systems (Cummins et al. 1998a). Several shortduration, limited-area studies have previously been conducted in Canada using provincial, territorial or private-sector lightning detection networks. Shortly after the Canadian Lightning Detection Network (CLDN) was established in 1998, the first national picture of lightning activity for the period 1989-1999 appeared in a Canadian Geographic article (Lanken, 2000). Lightning hotspots were shown to occur over the Prairie Provinces and southern Ontario. The first analysis of lightning characteristics across Canada was published by Burrows et al. (2002). Although only three years’ data were available for that study, a complex pattern emerged, showing strong regional, diurnal and seasonal features. This paper presents an updated climatology of cloud-ground lightning over Canada for 1999-2008 at a resolution of 20 km as detected by the CLDN. We highlight some of the main findings for flash density, occurrence, polarity, multiplicity, and first-stroke peak current. Due to the length of the material we cannot show the entire analysis. This is the subject of two papers in the peer review process at the time of this writing. For the sake of brevity we do not have a separate section summarizing conclusions here. 2. DATA and ANALYSIS
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".