<i>n</i>-Tree distance sampling for per-tree estimates with application to unequal-sized cluster sampling of increment core data
Bibliographic record
Abstract
Samples from the n trees nearest to a point or plot center are sometimes used to estimate per-tree values such as age or growth from increment cores. Clutter et al. (J.L. Clutter, J.C. Fortson, L.V. Pienaar, G.H. Brister, and R.L. Bailey. 1983. Timber management: a quantitative approach. John Wiley & Sons, New York) indicated that this procedure can be biased because it is more likely to sample large trees occupying large amounts of space. This sampling procedure falls into the category of n-tree distance sampling in which the nth closest tree to a point defines a plot radius that can be used to estimate number of trees or amount of volume per hectare. When a ratio of n-tree per-hectare estimates is used to estimate per-tree attributes, the resulting estimator is a weighted average in which weights are the inverse of the n-tree sampling plot size. Since this ratio estimator essentially weights observations inversely with plot size, it is not subject to the objections of Clutter et al. (1983). This estimator is used to estimate age by diameter at breast height class for eastern cottonwood (Populus deltoides Bartr. ex Marsh.) on the Cimarron National Grassland.
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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 | 0.026 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".