A novel ice storm manipulation experiment in a northern hardwood forest
Bibliographic record
Abstract
Ice storms are an important natural disturbance within forest ecosystems of the northeastern United States. Current models suggest that the frequency and severity of ice storms may increase in the coming decades in response to changes in climate. Because of the stochastic nature of ice storms and difficulties in predicting their occurrence, most past investigations of the ecological effects of ice storms across this region have been based on case studies following major storms. Here we report on a novel alternative approach where a glaze ice event was created experimentally under controlled conditions at the Hubbard Brook Experimental Forest, New Hampshire, USA. Water was sprayed over a northern hardwood forest canopy during February 2011, resulting in 7–12 mm radial ice thickness. Although this is below the minimum cutoff for ice storm warnings (13 mm of ice) issued by the US National Weather Service for the northeastern United States, this glaze ice treatment resulted in significant canopy damage, with 142 and 218 g C·m–2of fine and coarse woody debris (respectively) deposited on the forest floor, a significant increase in leaf-on canopy openness, and increases in qualitative damage assessments following the treatment. This study demonstrates the feasibility of a relatively simple approach to simulating an ice storm and underscores the potency of this type of extreme event in shaping the future structure and function of northern hardwood forest ecosystems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".