Scale and spatial structure effects on tree size distributions: implications for growth and yield modelling
Bibliographic record
Abstract
Diameter and other size distributions are extensively used in growth modelling. These are usually obtained from sample plot data and assumed to apply both at the stand level, of interest for production planning, and at the forest patch level, the level relevant for tree growth interactions. However, spatial correlation can cause distribution parameters and their estimates to vary with the extent of ground considered. Using mapped tree data from four forest stands in central Canada, it is shown that differences in DBH variance with plot size can be substantial. In addition, size correlations between neighbouring trees were positive, rather than negative as implied by current distance-dependent growth models. Biases in mean DBH are also found. It is proved that plot totals and frequencies are unbiased estimates of stand parameters, but variances and some other statistics are not. The expected variance is expressed in terms of plot size and shape and of second-order stand spatial structure properties. Some possible approaches for reducing bias in stand-level variance estimates are discussed, and the desirability of modelling microsite or genetic spatial correlations in individual-tree simulators is pointed out.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".