Trembling aspen responses to drought and defoliation by forest tent caterpillar and reconstruction of recent outbreaks in Ontario
Bibliographic record
Abstract
We investigated the long-term effects of drought and defoliation by forest tent caterpillars on trembling aspen radial growth in the province of Ontario using a dendroecological approach. Drought, as measured by Hogg’s climate moisture index (CMI), was found to have no discernible impact on aspen radial increment in either northeastern or northwestern Ontario during the study period 1930–2003. Forest tent caterpillar outbreaks were strongly decadal in periodicity in both regions and resulted in similar patterns of periodic variation in tree ring-width chronologies, indicating that, in humid environments prone to spatially synchronized tent caterpillar outbreaks, herbivory is the main factor limiting aspen radial growth. We show that the major decadal outbreak cycles of forest tent caterpillar can, by filtering with the computer program OUTBREAK, be reliably reconstructed from raw aspen ring-width chronologies. We determine the filtering parameters that give the most reliable reconstruction fit to observed patterns of outbreaks in each region. We show that the periodic outbreak signal is present even in areas where aerial surveyors frequently failed to detect significant levels of defoliation, and that the outbreak signal necessarily includes minor defoliation episodes that occur in between the major decadal outbreak cycles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".