Stand density biases the estimation of the site index especially on dry sites
Bibliographic record
Abstract
The site index (SI) has been widely used in forest management and silviculture. It relies on the assumption that the height of dominant trees in a stand is independent from the local density. However, research on climate change suggests that under certain moisture stress conditions, this may not hold. Here, based on 29 plots from five long-term research experiments, we tested the effect of local stand density on the SI of Norway spruce (Picea abies (L.) H. Karst). With generalized additive models (GAMM), we analyzed the effect of stand structure and climate predictors on SI. The two evaluated models revealed that local stand density and age had a significant effect on SI (p ≤ 0.001), showing a clear negative trend especially significant on sites with poor and dry soils, which may reduce the SI by a maximum of approximately 4 m for an increase in density of between 400 and 600 trees/ha. We stress that the physiological characteristics of Norway spruce, flat-rooting system and xeromorphism, especially when growing in pure stands, may explain these effects. Thus, density control and growth in mixtures may help to reduce the water stress and losses in height growth under future climate conditions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".