Design-unbiased estimation for point-to-tree distance sampling
Bibliographic record
Abstract
Point-to-tree distance sampling designs, sometimes also referred to as k-tree sampling or fixed-count sampling, are practical response design options for field sampling in forest inventories and ecological surveys. While practitioners accept and use several approaches to estimate stem density and other stand attributes, a major concern from a statistical point of view is the lack of a general unbiased estimator for this class of sampling strategies. In this paper we analyse point-to-tree distance sampling in the framework of design-based probabilistic sampling and present an unbiased estimator valid for estimation of any stand attribute. This estimator draws upon the idea of defining an inclusion zone around each tree. A tree is taken as a sample tree if a selected sample point falls into its inclusion zone. The size of the inclusion zone is therefore a measure of the individual tree's inclusion probability when sampling is done with random sample points. Once the inclusion probabilities are known for all sampled trees, the Horwitz-Thompson estimator can be used as an unbiased estimator for any stand variable. In point-to-tree distance sampling, the inclusion zone of a particular tree depends exclusively on the spatial arrangement of the neighbouring trees. Such inclusion zones are determined by k-order Voronoi polygons, where k is the number of trees being sampled per sample point. The approach, however, requires the positions of the k sample trees and a number of surrounding trees to be mapped. Field application is therefore difficult, but a comparison of plot designs by simulation studies in fully mapped stands can now also be done with an unbiased estimator for k-tree sampling.
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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 | 0.055 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".