Sector subsampling for basal area ratio estimation: an alternative to big BAF sampling
Bibliographic record
Abstract
Big basal area factor (big BAF) sampling is a widely used subsampling method to select measure-trees. Several studies have shown big BAF sampling to be an efficient sampling scheme. In this study, we use sector sampling (Smith et al. 2008, For. Sci. 54: 67–76) as an alternative subsample selection method. Based on some simulated mapped stands derived from three balsam fir (Abies balsamea (L.) Mill.) spacing trials in western Newfoundland, we show that sector subsampling is comparable to big BAF sampling in terms of estimated mean basal area ratios and their associated standard errors. Differences between big BAF sampling and sector sampling methods showed less than 1% difference across the three sites. As with big BAF sampling, changes in sample intensity had no significant (p < 0.05) effects on the accuracy of estimating mean biomass to basal area ratios and the resulting estimated mean biomasses per unit area.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".