Relationship between plot size and the variance of the density estimator in West African savannas
Bibliographic record
Abstract
The relationship between the variance of the density estimator and the plot size in forest inventories based on fixed area plots was characterized in West African savannas. Nine sites ranging from dry to moist savanna were surveyed, and the variance of the density estimator was assessed for varying sample plot sizes. An approximate theoretical expression of the variance was derived, taking into account the uncertainty on plot limits. In six sites, a Matérn process was fitted to the spatial pattern of trees. The point process was used to generate spatial patterns, yielding simulated values of the variance. A power function was also fitted to observed and simulated data. The theoretical expression and simulations showed that the contribution of uncertain plot limits to the variability of the density estimator was negligible with respect to the contribution of the spatial pattern of trees. The theoretical expression matched the data for small areas, but was inaccurate for large areas. The power function provided better fits. Nevertheless, the theoretical expression did not require any statistical fit to data and established a clear link between the spatial pattern of trees and the variance of the density estimator, with interpretable parameters.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".