Allometric Equations for Estimating Compartment Biomass and Stem Volume in Mature Hybrid Poplars: General or Site-Specific?
Bibliographic record
Abstract
We evaluated the extent to which general or site-specific allometric equations, using diameter at breast height (DBH) as a predictor, are more accurate for estimating stem volume, stem biomass, branch biomass, aboveground woody biomass, and coarse root biomass in 14 year-old plantations of Populus canadensis × Populus maximowiczii (clone DN × M-915508) located along an environmental gradient in southern Québec (eastern Canada). The effect of tree size and site on stem wood basic density, moisture content, and proportion of branch biomass was also evaluated. For stem volume, stem biomass, and aboveground biomass, site-specific and general models had comparable fit and accuracy, but lower Akaike’s Information Criterion (AICc) values were observed for the general models. For the branch and coarse root biomass, higher fit and accuracy and lower AICc values were observed for the site-specific models. Allometric trajectory changes (plastic allometry) across sites were mainly observed for coarse root biomass, branch biomass, and stem volume. On the low fertility site, allocation was increased to coarse roots and decreased to stem volume. Site-specific tradeoffs between tree architecture and stem wood density explained the relatively invariant allometry for the whole aboveground woody biomass across the plantation sites. On the high fertility sites, basic wood density was the lowest and declined as tree DBH increased. At all sites, stem wood moisture content and the proportion of branch biomass increased with DBH. Overall, this study showed that biomass allometry, tree architecture, and biomass quality are a function of both tree size and plantation environment in hybrid poplar. Allometric model selection (site-specific or general) should depend on the objective pursued (evaluation of yield, nutrient budget, carbon stocks).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".