Partitioning and predicting forage biomass from total aboveground biomass of regenerating tree species using dimensional analyses
Bibliographic record
Abstract
Foresters and wildlife biologists use biomass estimates as proxies of habitat structure, productivity, and carrying capacity. Determining biomass, however, is challenging without destructive harvests. We provide a dimensional analysis approach to partition browse biomass (BB) from total aboveground biomass (AGB) of six regenerating hardwoods in the Allegheny forests of Pennsylvania, USA. First, we determined the average diameter of browsed twigs for each species. Then, we created a subset of potential browsable twig and foliage biomass from total AGB in 439 individuals harvested within paired exclosure (fenced) and control (unfenced) plots at 15 sites. We fit species-specific allometric equations to estimate BB and AGB using basal diameter and height as predictors and tested the effects of fencing. Although overall stem height and BB were greater within exclosures, fencing did not significantly affect relationships between either predictor and BB or AGB, thereby enabling general and robust (R2 ≥ 0.80) equations for most species. Our work provides biomass equations for regionally dominant species and size classes that are underrepresented in the literature, yet critical to forest renewal and wildlife. Moreover, by sampling variable sites and levels of browse pressure, reported equations lessen site-specific biases. Finally, our methodology provides a template to generate forage biomass prediction equations for other plant and ungulate species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".