Comparison of plot- and stand-level projections of simulated loblolly pine (<i>Pinus taeda</i>) stands
Bibliographic record
Abstract
Estimating current stand conditions and value is often required for making management decisions. However, costs and logistical constraints make yearly inventory impractical in most settings, necessitating the use of growth and yield models. Projection of plots aggregated within stands, denoted as stand-level projection, and aggregation of individual projected plots within stands, denoted as plot-level projection, are two strategies used to predict stand parameters at future times. This study investigated the differences in the two projection strategies under differing levels of spatial heterogeneity and stand development. Simulated mapped stands and samples, along with three whole-stand models, were utilized to perform the comparisons. The results indicated that the two methods produced similar projections in terms of dominant height, basal area, and stems per hectare under most situations. As spatial heterogeneity increased, the stand-level projection indicated a significant bias of predicted total volume compared with the plot-level projection regardless of plot size as indicated by Jensen’s inequality. The model used made a noticeable impact on the differences, while thinning did not alter the patterns of observed differences. When implementing projections at the whole-stand level, careful consideration of stand heterogeneity and the growth and yield model is recommended.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".