The influence of stand and landscape characteristics on forest tent caterpillar (<i>Malacosoma disstria</i>) defoliation dynamics: the case of the 1999–2002 outbreak in northwestern Quebec
Bibliographic record
Abstract
The forest tent caterpillar (Malacosoma disstria Hbn.) is an eruptive forest insect common across North America and an important defoliator of trembling aspen (Populus tremuloides Michx.). Forest stands having suffered severe defoliations by the forest tent caterpillar over multiple years are known to incur reduced tree growth and increased tree mortality. In this study, we developed a predictive model of forest tent caterpillar defoliation dynamics using local and contextual variables expressing forest composition and structure, and their heterogeneity, at different scales. Of all scales considered (500, 1000, 1500, and 2000 m), contextual variables at 1500 m were found to have the greatest effect on defoliation dynamics. At this scale, we found that a greater proportion of preferred host trees in the landscape increased defoliation severity, but duration was modulated by compositional heterogeneity, where persistence was reduced in highly heterogeneous landscapes. Indeed, the likelihood of a single year of defoliation was much greater in highly diverse landscapes than the likelihood of multiple years of defoliation. These findings are consistent with ecological theory. Contrary to the expected result that older trees would be most susceptible, we found that “middle-aged” trees (~50 years) were most likely to be defoliated.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".