Ensemble lightning prediction models for the province of Alberta, Canada
Bibliographic record
Abstract
Lightning is a major cause of wildland fires in Canada. During an average year in the province of Alberta, 330 000 cloud-to-ground lightning strikes occur. These strikes are responsible for igniting 45% of reported wildfires (~450 fires) and 71% of area burned (~105 000 ha). Lightning-caused wildland fires in remote areas have large suppression costs and a greater chance of escaping initial attack when compared with human-caused fires, which are often located close to infrastructure and suppression resources. In this study, geographic and temporal covariates were paired with meteorological reanalysis and radiosonde observations to generate a series of 6-h and 24-h lightning prediction models valid from April to October. These models, based on cloud-to-ground lightning from the Canadian Lightning Detection Network, were developed and validated for the province of Alberta, Canada. The ensemble forecasts produced from these models were most accurate in the Rocky Mountain and Foothills Natural Regions, achieving hits rates of 85%. The Showalter index, latitude, elevation, longitude, Julian day and convective available potential energy were found to be highly important predictors. Random forest classification is introduced as a viable modelling method to generate lightning forecasts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".