A system for compatible prediction of total and merchantable volumes allowing for different definitions of tree volume
Bibliographic record
Abstract
A system of equations for compatible prediction of total and merchantable volumes that allows for different definitions of tree volume was developed in this study. The use of the developed system will allow the conversion and subsequent comparison of results from forest inventories using different definitions of tree volume (e.g., including or not the top material of the tree and (or) the stump, inside or outside bark). The compatibility between taper, total volume, and volume ratio equations is ensured by properly integrating the taper equation. The diameter under the bark at any height is modeled with the Demaerschalk taper equation, and the corresponding diameter over the bark is obtained by assuming that bark thickness is also modeled with Demaerschalk’s function. The set of equations that has contemporaneous cross-equation error correlation (known as nonlinear seemingly unrelated regression equations) was fit using nonlinear joint generalized least squares regression. The predictive ability was evaluated using an independent data set. The system is consistent and performs well when applied to maritime pine ( Pinus pinaster Ait.) trees in Portugal, showing better performance than do other total volume equations for maritime pine used in the latest Portuguese national forest 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".