Contributions of insects and droughts to growth decline of trembling aspen mixed boreal forest of western Canada
Bibliographic record
Abstract
Insects, diseases, fire and drought and other disturbances associated with global climate change contribute to forest decline and mortality in many parts of the world. Forest decline and mortality related to drought or insect outbreaks have been observed in North American aspen forests. However, little research has been done to partition and estimate their relative contributions to growth declines. In this study, we combined tree-ring width and basal area increment series from 40 trembling aspen (Populus tremuloides Michx.) sites along a latitudinal gradient (from 52° to 58°N) in western Canada and attempted to investigate the effect of drought and insect outbreaks on growth decline, and simultaneously partition and quantify their relative contributions. Results indicated that the influence of drought on forest decline was stronger than insect outbreaks, although both had significant effects. Furthermore, the influence of drought and insect outbreaks showed spatiotemporal variability. In addition, our data suggest that insect outbreaks could be triggered by warmer early spring temperature instead of drought, implicating that potentially increased insect outbreaks are expected with continued warming springs, which may further exacerbate growth decline and death in North America aspen mixed forests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 source (direct Gemma or distilled Codex), 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".