Bibliographic record
Abstract
Sampling strategies commonly used for coarse woody debris (CWD) inventories, including line intersect sampling (LIS), typically require large sample sizes to estimate aggregate volume with reasonable precision. Line intersect distance sampling (LIDS) is a recently developed strategy based on a probability proportional-to-volume design and a linear sampling unit. In principle, the design augments the precision of volume estimators by increasing the intensity with which bulkier particles are sampled, while the transect-based protocol facilitates the search for qualifying particles. This study reports on the relative performances of LIDS and LIS in seven stands in Montana, USA. Particles selected by LIDS were consistently less numerous but larger in cross section than those selected at the same locations by LIS. In timed field trials, LIDS required more time than LIS, but CWD volume estimates from LIDS were generally more precise, more than offsetting the time differential. Conversely, aggregate length and abundance of CWD were generally estimated more efficiently with LIS. Results suggest that LIDS permits more efficient use of survey resources than LIS where CWD inventories focus on parameters relating to volume, biomass, or carbon. However, the constant volume factor design of LIDS is not advantageous where CWD frequency is of central interest.
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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 | 0.064 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".