Allometry for estimating aboveground tree biomass in tropical and subtropical eucalypt woodlands: towards general predictive equations
Bibliographic record
Abstract
A fundamental tool in carbon accounting is tree-based allometry, whereby easily measured variables can be used to estimate aboveground biomass (AGB). To explore the potential of general allometry we combined raw datasets from 14 different woodland species, mainly eucalypts, from 11 sites across the Northern Territory, Queensland and New South Wales. Access to the raw data allowed two predictor variables, tree diameter (at 1.3-m height; D) and tree height (H), to be used singly or in various combinations to produce eight candidate models. Following natural log (ln) transformation, the data, consisting of 220 individual trees, were re-analysed in two steps: first as 20 species–site-specific AGB equations and, second, as a single general AGB equation. For each of the eight models, a comparison of the species–site-specific with the general equations was made with the Akaike information criterion (AIC). Further model evaluation was undertaken by a leave-one-out cross-validation technique. For each of the model forms, the species–site-specific equations performed better than the general equation. However, the best performing general equation, ln(AGB) = –2.0596 + 2.1561 ln(D) + 0.1362 (ln(H))2, was only marginally inferior to the species–site-specific equations. For the best general equation, back-transformed predicted v. observed values (on a linear scale) were highly concordant, with a slope of 0.99. The only major deviation from this relationship was due to seven large, hollow trees (more than 35% loss of cross-sectional stem area at 1.3 m) at a single species–site combination. Our best-performing general model exhibited remarkable stability across species and sites, when compared with the species–site equations. We conclude that there is encouraging evidence that general predictive equations can be developed across sites and species for Australia’s woodlands. This simplifies the conversion of long-term inventory measurements into AGB estimates and allows more resources to be focused on the extension of such inventories.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".