Approximating covariances between nested plot sizes in forest inventory
Bibliographic record
Abstract
The use of nested plot sizes in forest inventories that encounter a wide range of conditions is relatively common. In straightforward situations, data from the different nested plot sizes are usually combined to create a single plot observation for estimation purposes. However, circumstances may occur where nested plot size estimates are obtained separately and subsequently combined via addition to obtain the final result. In these cases, covariances among the nested plots must be considered in the calculation of estimator variance. However, there are several potential approaches that might be considered given that only partial information is known for all but the smallest nested plot size. In this paper, three approaches to estimating the covariance were examined: (1) a modified form of a partially-dependent sample adjustment factor method, (2) explicit subsetting of trees to the area in common among nested plots, and (3) typical covariance estimation ignoring the lack of common area basis. Although the adjustment factor and subsetting methods showed strong consistency in outcomes, the estimated covariances were much smaller than those from ignoring the area basis issue. A subsequent simulation exercise revealed the most accurate covariances were obtained by ignoring the area issue. Thus, covariance estimation calculations can proceed without the additional complications of accounting for different nested plot sizes.
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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 | 0.021 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".