Integrated system of equations for estimating stem volume, density, and biomass for Australian redcedar (<i>Toona ciliata</i>) plantations
Bibliographic record
Abstract
A system of equations is proposed to assess the stem wood density variation of Toona ciliata M. Roem. growing in Brazilian plantations. As a taper function, a third-degree polynomial was fitted and the stem radius squared (r2), the dependent variable, was estimated as a function of diameter at breast height (dbh), total height (ht), and radius (r) at height (h). A nonlinear function was fitted to estimate wood density variation, having as the independent variable the ratio of r to h. The stem mass was estimated by integrating the product of stem volume and wood density. Stem measurements from a group of 72 trees of T. ciliata were used to fit the taper equation. A group of six trees was selected and a wood density database was created using X-ray technology. Both the taper and the nonlinear functions performed well in estimating the radius and the wood density. The within-tree wood density systematically increased from pith to bark and from the base to the top of the tree. With the density varying from base to top, the estimated mass of the stem, compared with the mass estimated using wood density value at dbh, had a bias of 4.2%. When the density variations from base to top and from pith to bark of the tree were considered, the estimated mass had a bias of 1.5%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".