Interannual to decadal changes in area burned in Canada from 1781 to 1982 and the relationship to Northern Hemisphere land temperatures
Bibliographic record
Abstract
ABSTRACT Aim Temporal variability of annual area burned in Canada (AAB‐Can) from ad 1781 to 1982 is inferred from tree‐ring width data. Next, correlation analysis is applied between the AAB‐Can estimates and Northern Hemisphere (NH) warm season land temperatures to link recent interannual to decadal changes in area burned with large‐scale climate variations. The rationale in this use of tree rings is that annual radial increments produced by trees can approximate area burned through sensing climate variations that promote fire activity. Location The statistical reconstruction of area burned is at the scale of Canada. Methods The data base of total area burned per year in Canada is used as the predictand. A set of 53 multicentury tree‐ring width chronologies distributed across Canada is used as predictors. A linear model relating the predictand to the tree‐ring predictors is fitted over the period 1920−82. The regression coefficients estimated for the calibration period are applied to the tree‐ring predictors for as far back as 1781 to produce a series of AAB‐Can estimates. Results The AAB‐Can estimates account for 44.1% of the variance in the observed data recorded from 1920 to 1982 (92.2% after decadal smoothing) and were verified using a split sample calibration‐verification scheme. The statistical reconstruction indicates that the positive trend in AAB‐Can from c . 1970–82 was preceded by three decades during which area burned was at its lowest during the past 180 years. Correlation analysis with NH warm season land temperatures from the late 18th century to the present revealed a positive statistical association with these estimates. Main conclusions As with previous studies, it is demonstrated that the upward trend in AAB‐Can is unlikely to be an artefact from changing fire reporting practices and may have been driven by large‐scale climate variations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".