A line intersect distance sampling strategy for downed wood inventory
Bibliographic record
Abstract
Perpendicular distance sampling (PDS) has emerged as a compelling alternative to line intersect sampling (LIS) for the inventory of forest fuels and other downed woody materials (DWM), particularly where the aggregate volume of DWM is of primary interest. This article develops a selection protocol and design-unbiased estimators for a new probability proportional-to-volume sampling strategy, termed line intersect distance sampling (LIDS). LIDS combines the distance sampling protocol of PDS with the transect sampling protocol of LIS and provides unbiased estimates of aggregate DWM volume from counts of selected logs or log fragments. Simulations indicate that LIDS along multidirectional (e.g., Y-shaped) transects should perform similarly to PDS in terms of sampling error; however, it remains unclear how LIDS and PDS compare with LIS, especially when interest is attached to multiple DWM population parameters. It is argued that LIDS will be most useful in reducing implementation errors, particularly detection errors, relative to PDS under limited visibility field conditions.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".