Comparison of forest age estimators using <i>k</i>-tree, fixed-radius, and variable-radius plot sampling
Bibliographic record
Abstract
Quantifying the age characteristics of a forest can provide valuable information about the forest’s impact on the environment. For instance, the age of a forest can affect the ecosystem’s carbon exchange, soil enzyme activity, and biodiversity. In this paper, we investigate the use of different sampling methods to estimate the age characteristics of three simulated ponderosa pine (Pinus ponderosa Dougl. ex P. Lawson & C. Lawson) forests having different spatial and age patterns. This includes estimating the mean tree age and the age-class distribution of the trees in the forest. The trees in the sample are selected using k-tree sampling, fixed-radius plot sampling, or variable-radius plot sampling, and we compare the properties of the resulting estimators via design-based and model-based approaches. Analyses of the different sampling methods applied to the three forests suggest that the estimator associated with k-tree sampling, with the addition of a few extra trees per plot, is feasible for forests having a spatially mosaic or random spatial pattern. The estimator associated with fixed-radius plot sampling performed well for the forest having a clustered spatial pattern.
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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 | 0.031 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".