Large forest fires in Canada and the relationship to global sea surface temperatures
Bibliographic record
Abstract
Relationships between variations in peak Canadian forest fire season (JJA) severity and previous winter (DJF) global sea surface temperature (SST) variations are examined for the period 1953 to 1999. Coupled modes of variability between the seasonal severity rating (SSR) index and the previous winter global SSTs are analyzed using singular value decomposition (SVD) analysis. The robustness of the relationship is established by the Monte Carlo technique. The importance of the leading three SVD modes, accounting for approximately 90% of the squared covariance, to Canadian summer forest fire severity is identified. The first mode relates strongly to the global long‐term trend, especially evident in the warming of the Southern Hemisphere oceans, and shows significant positive correlation in the forested regions of northwestern, western and central Canada, while southern B.C., the extreme northwest coastal regions of B.C. and Yukon, and the Great Lakes region are identified as having significant negative correlation. The second mode relates to the multidecadal variation of Atlantic SST (AMO) and shows highly significant negative correlation extending from the western NWT and Canadian Prairie Provinces across northern Ontario and Quebec. The third mode is related to Pacific Ocean processes and the interrelationship between El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) and shows strong positive correlation in western Canada and negative correlation in the lower Great Lakes region of southern Ontario and southern Quebec. A 6‐month lag relationship between Canadian forest fire variability and large‐scale SSTs may provide the basis for developing long‐range forecasting schemes for fire severity in Canada.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".