Designing circular fixed-area plots in large-scale forest inventories: effect of horizontal distance measurement uncertainty and tree position pattern
Bibliographic record
Abstract
Plot design is one of the key elements that must be defined in forest inventories. This is particularly challenging in large-scale inventories, as both forest variability and measurement uncertainty generally increase with scale. Nonetheless, plot design is usually based exclusively on targeting (i) an average minimum number of trees to reduce the random measurement errors, and (ii) an average maximum number of trees to optimize the efficiency of fieldwork, while ignoring the uncertainty in tree position measurement. The present study focused on the effect of horizontal distance measurement errors on stand level estimates in large-scale forest inventories including circular fixed-area plots. The error was characterized in forests with non-regular (natural stands) and regular (plantations) patterns of tree positions. The effect on stand volume, stand basal area, and stand density estimates was simulated using Monte Carlo techniques. Different horizontal distance measurement uncertainty was observed in natural stands and plantations. However, similar effects were observed in the three stand variables estimates for both tree spacing patterns, with stabilization of errors for radii between 12 and 20 m. Doubling or halving the error uncertainty yielded similar results. The proposed method can help with selecting plot size in forest inventories based on circular fixed-area plots.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".