Subalpine forest damage from a severe windstorm in northern Colorado
Bibliographic record
Abstract
As windstorm intensity increases above some threshold, disturbance spread and damage patterns are expected to be less strongly shaped by preblowdown forest composition and structure than by the pattern of the storm itself. We examined this generalization by analyzing differences in wind damage among tree species and stands following a severe blowdown in 1997 affecting over 10 000 ha of subalpine forest in the Routt Divide area of northern Colorado, U.S.A. Individual tree traits such as species, height, and status as standing dead or alive strongly influenced the amount and type (uprooting vs. snapping) of wind damage. Populus tremuloides Michx. exhibited much less uprooting and overall damage than the conifers. Among the canopy trees of the conifer species, Pinus contorta Dougl. ex. Loud and Abies lasiocarpa (Hook.) Nutt. sustained the lowest and highest rates of snapping, respectively. Standing dead conifers were more likely to be snapped than uprooted, and taller trees were more likely to be damaged than shorter trees. Stand-level characteristics such as stand density, amount of dead basal area, and species composition were predictive of the amount of wind damage for areas of moderate- but not high-severity blowdown. Even in such an extreme windstorm as the Routt blowdown, which had estimated wind speeds of 200250 km/h, individual tree attributes and stand-level characteristics significantly influenced the severity and type of wind damage.
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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 | 0.000 | 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".