Perpendicular distance sampling: an alternative method for sampling downed coarse woody debris
Bibliographic record
Abstract
Coarse woody debris (CWD) plays an important role in many forest ecosystem processes. In recent years, a number of new methods have been proposed to sample CWD. These methods select individual logs into the sample using some form of unequal probability sampling. One concern with most of these methods is the difficulty in estimating the volume of each log. A new method of sampling CWD that addresses this issue is proposed. This method samples each log with probability proportional to the volume of each piece of CWD. While this method generally has a smaller variance than the existing methods, the primary advantage is that a design-unbiased estimator of CWD volume is achieved without ever actually measuring the volume of any logs. This method, referred to as perpendicular distance sampling (PDS), is compared with three existing sampling techniques for CWD using a simulation study on a series of artificial populations. In every case, the variance of the PDS estimator of CWD volume was smaller than the variance of the competing methods, but the difference in the variance was not large between PDS and two of the competing methods. When estimating the number of pieces of CWD, the variance of the PDS estimator was one of the largest amongst the tested methods. An equally important result is that the variant of line intersect sampling used in this study, where the orientation of the line is the same at all sample points, performed poorly in every situation. This and other problems suggest that the suitability of this sampling technique for estimating CWD is questionable.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".