Use of growth curve derivatives to illustrate acceleration and deceleration of growth in young plantations under variable competition
Bibliographic record
Abstract
Deceleration of growth rates can give an indication of competition and the need for thinning in early years but can be difficult to detect. We computed the first and second derivatives of the von Bertalanffy – Richards equation to assess impacts of density and vegetation control in young plantations in western Oregon. The first derivative describes the response in growth and the second derivative describes the change in growth over time. Three sets of density experiments were used: (i) pure Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), (ii) mixed Douglas-fir and grand fir (Abies grandis (Dougl. ex D. Don) Lindl.), and (iii) mixed western hemlock (Tsuga heterophylla (Raf.) Sarg.) and red alder (Alnus rubra Bong.). Original planting densities ranged from 475 to 85 470 trees·ha–1 (4.6 m × 4.6 m to 0.34 m × 0.34 m spacing); western hemlock and red alder plots were weeded and unweeded. For the highest densities, the second derivative was rarely above zero for any of the time periods, indicating that the planting densities were too high for tree growth to enter an exponential phase. As expected, the lower the density, the greater and later the peak in growth for both the first and second derivatives. Weeding increased the growth peaks, and peaks were reached earlier in weeded than in unweeded plots. Calculations of this sort may help modelers identify when modifiers for competition and density are needed in growth equations. Specific applications help define onset of competition, precise determining of timing of peak growth, period of acceleration of growth, and interaction of spacing and age in determination of peaks of increment or acceleration or deceleration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".