Lightning ignition efficiency in Canadian forests
Bibliographic record
Abstract
Background: Lightning-caused fires have a driving influence on Canadian forests, being responsible for approximately half of all wildfires and 90% of the area burned. We created a climatology (2000-2020) of daily lightning efficiency (i.e., the ratio of cloud-to-ground lightning flashes to lightning-caused wildfires that occurred) over the meteorological summer for four ecozones and a subset of British Columbia (BC) ecoprovinces. We estimated lightning efficiency using data from the Canadian Lightning Detection Network and the Canadian National Fire Database. We used the ERA5 reanalysis as inputs for fuel moisture variables (i.e., Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), and Drought Code (DC)) from the Canadian Forest Fire Weather Index (FWI) System, as well as variables relating to the amount of precipitation and lightning flashes. We examined relationships between lightning efficiency, day-of-year, and the above variables using a combination of linear models, Spearman's correlations, and Random Forest (RF) regression. Results: Lightning efficiency increased non-linearly (i.e., quadratic) over the summer in the Montane Cordillera Ecozone, and decreased linearly in the Boreal Plains and Boreal Shield West. Lightning efficiency in the Boreal Shield East showed a slight decline over the summer; however, this model was not significant. DMC and DC were more strongly correlated with lightning efficiency than FFMC in most zones. We ran RF regression both with and without DC (because of multicollinearity with day-of-year), and day-of-year, DMC, and DC (when present) were the most important variables for all ecozones, while results were more variable for the ecoprovinces. Conclusions: Lightning efficiency, and, thus, the probability of a lightning strike igniting a wildfire, changes over the summer and varies by region. Therefore, models predicting lightning-caused fire occurrence, or other similar applications involving lightning ignition, may benefit by accounting for seasonal lightning efficiency in addition to the traditional fuel moisture variables. Our work is generally consistent with findings from more localized studies relating to lightning-caused fires. Supplementary Information: The online version contains supplementary material available at 10.1186/s42408-025-00376-1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".