Quantifying size-asymmetric growth among individual beech trees
Bibliographic record
Abstract
We modelled the growth of individual trees in populations of European beech (Fagus sylvatica L.) grown under different thinning regimes using a modified Richards equation. The effect of competition on growth was modelled by coupling the n individual equations simultaneously with a saturation term. By assuming that the growth of an individual within the population is a function of its size to a power a, a measure of the growth advantage of larger individuals (size-asymmetric growth) is provided. If a > 1, larger trees have a disproportionate advantage in growth and by inference, in competition. The degree of size-asymmetric growth, a, exceeded one in stands with large size variability and increased significantly at increasing density. This suggests that the predominant mode of competition is size asymmetric and that this size asymmetry increases with density. A measure of growth asymmetry is more informative than static measures of size inequality in understanding the growth dynamics of managed forest stands. Since a provides a measure of the relative importance of above- versus below-ground competition, it may be useful in interpreting the growth dynamics of forest stands and may provide an additional level of information for modellers of forest growth.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".