Effects of an experimental ice storm on forest canopy structure
Bibliographic record
Abstract
Intermediate disturbances are an important component of many forest disturbance regimes, with effects on canopy structure and related functions that are highly dependent on the nature and intensity of the perturbation. Ice storms are an important disturbance mechanism in temperate forests that often result in moderate-severity, diffuse canopy damage. However, it has not previously been possible to distinguish the specific effect of ice storm intensity (as ice accretion) from predisturbance stand characteristics and physiographic factors. In this study, we utilized a novel experimental ice storm treatment to evaluate the effects of variable ice accretion levels on forest canopy structure. Our results verified significant impacts of ice storm disturbance on near-term canopy structural reorganization. Canopy openness, light transmission, and complexity increased significantly relative to predisturbance baselines and undisturbed controls. We documented variable impacts with disturbance intensity, as significant canopy changes largely occurred with ice accretion levels of ≥12.7 mm. Repeated ice storm disturbance (two consecutive years) had marginal, rather than compounding, effects on forest canopy structure. Our findings are relevant to understanding how ice storms can affect near-term forest canopy structural reorganization and ecosystem processes and add to a growing base of knowledge on the effects of intermediate disturbances on canopy structure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".