A field study on critical height sampling with antithetic variate and importance sampling in estimating stand volume
Bibliographic record
Abstract
Abstract Stand volume is usually estimated by volume or taper models. Models may not always be available or reliable for estimating stand volume. Critical Height Sampling with Antithetic Variate and Importance Sampling (AICHS), built on Critical Height Sampling (CHS), is a sampling method that estimates stand volume without relying on volume models. AICHS applies Importance Sampling and antithetic variate with a proxy taper function to determine a cross-sectional area on a tree stem from which its height is measured to reduce viewing angles. It has never been studied in the field. The objectives of this field study were to compare AICHS, AICHSCYL (simplification of AICHS assuming a cylindrical stem below breast height), and CHS in estimating mean and precision of stand volume, their relative efficiency, and measurement issues. We established 65 plots in planted forests of two coniferous tree species in central Taiwan. At each plot, we used three basal area factors (BAFs) to select trees for estimating stand volume with AICHS, AICHSCYL, and CHS. Results showed that mean stand volume of AICHS was less consistent across the three BAFs than CHS. Mean stand volume of AICHS was larger than that of AICHSCYL by 25–35 m3 ha−1. The precision of AICHS was 5.8–7.1%, but it could be lower or higher than CHS depending on the tree species and the choice of BAFs. AICHS is ~29%–49% less efficient than AICHSCYL and CHS. For AICHS, 50% of the antithetic critical height measurements were below about half of total tree height, and ~42% of the measurements were in tree crowns. In summary, our field assessment suggests that AICHS might have limited field applications given its inconsistent mean estimates and precision, lower efficiency, and potential sighting issues due to crown obstructions. Nonetheless, our study highlights the importance of this field study to understand field potentials and limitations in AICHS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".