Modeling ice storm damage to a mature, mixed-species hardwood forest in eastern Ontario
Bibliographic record
Abstract
In January 1998, the worst ice storm of the last century hit regions of southeastern Ontario, Québec, New Brunswick, and the northeastern United States. Using standard multiple regression and classification tree models, we examined the ice damage suffered by trees in a mature, deciduous forest in eastern Ontario at two scales: plot (5 m radius) and individual tree. Canopy trees were damaged significantly more than mid-story trees and there were significant differences among tree species in damage susceptibility. At the plot scale, the best predictors of damage were average tree size and plot species evenness. Plots dominated by large trees were damaged more than those dominated by small trees and plots with higher species evenness suffered higher levels of damage than did less even plots. Models incorporating damage to neighbouring plots explained more variance than did models without the neighbour information. At the individual tree scale, damage suffered by the dominant canopy tree species, sugar maple, was best predicted by tree size. Damage suffered by the dominant mid-story tree species, ironwood, was best predicted by neighbour information and tree size. Disturbances that differentially affect canopy and mid-story layers have been shown to accelerate forest succession by creating light gaps. However, given the species composition and structure of our study forest, we feel that this disturbance will not overly influence forest succession in mature, deciduous forests in eastern Ontario.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".