Data from: The influence of landscape context on short- and long-term forest change following a severe ice storm
Bibliographic record
Abstract
1. When deforestation results in small forest fragments surrounded by a non-forest matrix, forest stands within these fragments experience changes in structure and community composition. They also continue to experience natural disturbances like hurricanes and ice storms. It is unclear whether the landscape context of forest stands influences plant community response to natural disturbances. 2. Using data from surveys of forested plots in the years immediately following and 19 years after a severe ice storm, we measured changes in woody stem density, species richness, and beta diversity. 3. Plots with greater storm damage had greater gains in stems and species, and greater shifts in community composition. In addition, there were interactions between the degree of storm damage and landscape context. The short-term effects of storm damage were magnified in plots with less forest on the surrounding landscape and farther from the forest edge. In plots with high damage, a return towards pre-storm conditions over the long-term occurred more often in plots farther from the forest edge compared to those close to the edge. 4. Synthesis: Future climate scenarios predict increases in severe weather and accompanying ecosystem disturbance. Our results show that it is important to consider landscape context when assessing the response of forest communities to such 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.030 |
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".