Changes in understory composition following catastrophic windthrow and salvage logging in a subalpine forest ecosystem
Bibliographic record
Abstract
Catastrophic windthrow and postdisturbance salvage logging each have the potential to profoundly influence understory vegetation communities. This study compared understory vegetation cover, composition, and diversity in Routt National Forest, a subalpine forest in northwestern Colorado that sustained a 10 000 ha blowdown in 1997 and was partially salvage logged in 1999. Understory and edaphic variables were measured in five heavily wind-disturbed Picea–Abies stands, five stands salvage logged 20 months after the blowdown, and five intact stands. Understory species cover and diversity were greater in blown down areas than in salvage-logged or control areas. Community composition of each treatment area was distinct and related to a gradient in organic soil depth, which reflected the severity of understory disturbance. Composition and diversity in blowdown areas relative to control areas stabilized in the 5 years following the blowdown, but vegetation cover continued to increase. Blowdown areas contained early and late successional species. Salvage-logged areas exhibited a shift towards graminoid dominance. This structural change could delay future conifer seedling establishment. The interaction among disturbance severity, understory vegetation composition, and regeneration dynamics should be considered in future decisions to salvage log similar areas because the long-term effects of salvage logging are unknown.
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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 | 0.000 | 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".