Forest sampling combining fixed- and variable-radius sample plots
Bibliographic record
Abstract
We examine the statistical properties of a forest sampling strategy that combines methods of fixed- and variable-radius plot sampling. Circular fixed-radius plots are established at the same locations as variable-radius plots to take advantage of their known efficiencies for unbiasedly estimating stem density and basal area, respectively. The design eliminates the need for measuring stem diameters, except to check borderline trees on variable-radius plots. Separate controls on allowable error for stem density and basal area estimates are possible. An unbiased estimator of volume (Vol) is derived that uses an existing volume equation having the form Vol = a + bd2h, where d is tree diameter at breast height, h is tree height, and a and b are model parameters. Calculation of volume requires only the measurement of heights for those trees tallied on the variable-radius plots. Properties of the estimator are demonstrated for a mixed-species hardwood forest in the southern Appalachian Mountains of North Carolina, USA.
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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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".