Is the health of British Columbia's forests being influenced by climate change? If so, was this predictable?
Bibliographic record
Abstract
Abstract Over 14 million hectares of lodgepole pine-dominated forests in British Columbia (BC), Canada, have been severely impacted by the current mountain pine beetle epidemic. Simultaneously, a Dothistroma needle blight epidemic in northwest BC has been responsible for killing thousands of hectares of pine plantations and has even resulted in the death of mature trees, which is unprecedented. Both of these globally significant forest pest epidemics have been linked to climate change. The beetle epidemic has grossly exceeded the scale of all previously recorded outbreaks, in large part due to a lack of cold winters. In this sense, the link to global warming is relatively straightforward and foreseeable. Of all climate change projections, the prediction of an increase in winter temperatures has been associated with as high a degree of confidence as any. The Dothistroma needle blight epidemic and its link to climate change were not so predictable. Based on weather records over the past four decades, short-term increases in mean summer precipitation correlate closely with historical records of Dothistroma outbreaks in the northwest. The current most severe outbreak has occurred during a prolonged period of above-average summer precipitation. An increase in summer precipitation would more typically be thought of as beneficial for forests, but that increase in moisture has improved the conditions for a pathogen that has outweighed any benefits. Similar trends of increasing incidence and severity for other forest pathogens in BC appear in areas that have been receiving increased summer precipitation. Conversely, in the southern interior of the province decreased summer precipitation and increased drought conditions are resulting in stressed trees which can favour root diseases. Early indications are that climate change will have profound effects on forest health. Some effects will be more predictable than others.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".