Effects of an intense ice storm on the structure of a northern hardwood forest
Bibliographic record
Abstract
A major ice storm in January 1998 provided an opportunity to study the effects of a rare, intense disturbance on the structure of the northern hardwood forest canopy. Canopy damage was assessed using visual damage classes within watersheds of different ages at the Hubbard Brook Experimental Forest (HBEF) and changes in leaf area index in two of these watersheds. Ice thickness was measured, and ice loads of trees were estimated using regression equations. In the 60- to 120-year-old forests (mean basal area 26 m 2 ·ha 1 ), damage was greatest in trees >30 cm diameter at breast height and at elevations above 600 m. Of the dominant tree species, beech (Fagus grandifolia Ehrh.) was the most damaged, sugar maple (Acer saccharum Marsh.) was the most resistant, and yellow birch (Betula alleghaniensis Britt.) was intermediate. Trees with advanced beech bark disease experienced heavier ice damage. Little damage occurred in the 14-year-old forest, while the 24- to 28-year-old forest experienced intense damage. In the young stands of this forest, damage was greatest between 600 and 750 m, in trees on steep slopes and near streams, and among pin cherry (Prunus pensylvanica L.). Recovery of the canopy was tracked over three growing seasons, and root growth was monitored 1 year after the storm. Because of the high density of advance regeneration from beech bark disease and root sprouting potential in ice-damaged beech, HBEF will likely see an increase in beech abundance in older forests as a result of the storm. There will also be a more rapid change from pioneer species to mature northern hardwoods in the younger forests. These predictions illustrate the ability of rare disturbances to increase heterogeneity of forest structure and composition in this ecosystem, especially through interactions with other disturbances.
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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) | 0.001 | 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".