Validation and refinement of allometric equations for roots of northern hardwoods
Bibliographic record
Abstract
The allometric equations developed by Whittaker et al. (1974. Ecol. Monogr. 44: 233–252) at the Hubbard Brook Experimental Forest have been used to estimate biomass and productivity in northern hardwood forest systems for over three decades. Few other species-specific allometric estimates of belowground biomass are available because of the difficulty in collecting the data, and such equations are rarely validated. Using previously unpublished data from Whittaker’s sampling effort, we extended the equations to predict the root crown and lateral root components for the three dominant species of the northern hardwood forest: American beech ( Fagus grandifolia Ehrh.), yellow birch ( Betula alleghaniensis Britt), and sugar maple ( Acer saccharum Marsh.). We also refined the allometric models by eliminating the use of very small trees for which the original data were unreliable. We validated these new models of the relationship of tree diameter to the mass of root crowns and lateral roots using root mass data collected from 12 northern hardwood stands of varying age in central New Hampshire. These models provide accurate estimates of lateral roots (<10 cm diameter) in northern hardwood stands >20 years old (mean error 24%–32%). For the younger stands that we studied, allometric equations substantially underestimated observed root biomass (mean error >60%), presumably due to remnant mature root systems from harvested trees supporting young root-sprouted trees.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".