An improved estimator for the sampling error of local competition variables
Bibliographic record
Abstract
We present a revised estimator for the sampling error of local competition variables that builds on the conceptual framework given by Stage and Wykoff (Stage, A.R., and Wykoff, W.R. For. Sci. 44(2): 224–238, 1998). Accurate estimation of the sampling error of local competition variables is a requisite for most approaches that correct the effects of measurement error in model fitting and application. Our revision addresses the bias inherent in Stage and Wykoff’s estimator due to the overlapping of random samples that are constrained to include a subject tree. We also argue that the adjustment that Stage and Wykoff used to account for the absence of treeless plots (zero truncation) is unnecessary. We illustrate the performance of the new estimator and the estimator of Stage and Wykoff through simulation. For a hypothetical Poisson forest (800 trees·ha−1; mean diameter at breast height, 9.8 cm), bias is negligible for the new estimator, and variance is reduced by 92%.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".