Greater risk of physical damage caused by debris fall to understory plants and tree seedlings in old-growth forests than in young forests
Bibliographic record
Abstract
Falling canopy debris causes injury and mortality of tree seedlings and understory plants in a wide variety of forests. Canopy structure and dynamics differ between young and old-growth forests: old forests are taller and have more aboveground biomass and greater annual mortality of bole biomass. I predicted that risk of damage caused by debris fall in the understory is greater in old-growth forests than in young forests. I tested this prediction by tracking for 1 year the fates of artificial seedlings placed in young (stand age 31 to 61 years) and old-growth (stand age circa 500 years) Pseudotsuga–Tsuga forests. The risk of physical damage caused by debris fall in old-growth forests was significantly greater than in young forests (P = 0.001). Seedling models were damaged by falling debris at a rate of 4.4%·year−1 and 0.8%·year−1 in old-growth and young forests, respectively. More seedling models were damaged by fallen coarse woody debris in old-growth forests than in young forests, although this trend was not significant (P = 0.134). Approximately 25% of seedling models in both young and old-growth forests were damaged by something other than fallen canopy debris, most likely snow accumulation.
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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 | 0.001 |
| 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 | 0.001 | 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".