Differences in stem taper of loblolly pine (<i>Pinus taeda</i>) grown in Coastal Plains and Southern Appalachians regions of the United States
Bibliographic record
Abstract
Taper equations are useful to forest managers as they allow the prediction of diameter at any height or height to any diameter along the stem and facilitate the estimation of total or merchantable volume. These equations are typically species-specific and fit with data from a small geographic area. Even when the models are developed with regional datasets, most taper equations overlook the difference in tree shape across geographic regions and how the errors propagate when models fit to one region is applied to another region. This study aimed to find the difference in stem taper and volume of loblolly pine ( Pinus taeda) across two ecological regions and the associated environmental factors in the southern United States. Results showed considerable differences in the taper and volume of trees between the regions. Prediction errors increased when models trained on one region were applied to the other region compared to region-specific validation. Errors were largest when the model was developed using data from the Coastal Plains and applied to trees in the Southern Appalachians. This suggests that forest managers should consider the source of model fitting data when selecting taper models to accurately estimate the total or sectional volumes.
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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 | 0.000 | 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) | 0.000 | 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".