Prediction of Seasonal Forest Fire Severity in Canada from Large-Scale Climate Patterns
Bibliographic record
Abstract
Abstract An empirical scheme for predicting the meteorological conditions that lead to summer forest fire severity for Canada using the multivariate singular value decomposition (SVD) has been developed for the 1953–2007 period. The levels and sources of predictive skill have been estimated using a cross-validation design. The predictor fields are global sea surface temperatures (SST) and Palmer drought severity index. Two consecutive 3-month predictor periods are used to detect evolving conditions in the predictor fields. Correlation, mean absolute error, and percent correct verification statistics are used to assess forecast model performance. Nationally averaged skills are shown to be statistically significant, which suggests that they are suitable for application to forest fire prediction and for management purposes. These forecasts average a 0.33 correlation skill across Canada and greater than 0.6 in the forested regions from the Yukon, through northern Prairie Provinces, northern Ontario, and central Quebec into Newfoundland. SVD forecasts generally outperform persistence forecasts. The importance of the leading two SVD modes to Canadian summer forest fire severity, accounting for approximately 95% of the squared covariance, is emphasized. The first mode relates strongly to interdecadal trend in global SST. Between 1953 and 2007 the western tropical Pacific, the Indian, and the North Atlantic Oceans have tended to warm while the northeastern Pacific and the extreme Southern Hemisphere oceans have shown a cooling trend. During the same period, summer forest fire exhibited increased severity across the large boreal forest region of Canada. The SVD diagnostics also indicate that the El Niño–Southern Oscillation and the Pacific decadal oscillation play a significant role in Canadian fire severity. Warm episodes (El Niño) tend to be associated with severe fire conditions over the Yukon, parts of the northern Prairie Provinces, and central Quebec. The linearity of the SVD manifests opposite response during the cold (La Niña) events.
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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 | 0.001 | 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 | 0.001 |
| 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".