Comparison of taper functions applied to eucalypts of varying genetics in Brazil: application and evaluation of the penalized mixed spline approach
Bibliographic record
Abstract
Taper functions have been widely used with an existing array of model forms and methods; however, comparisons of contrasting statistical methods have been more limited. This study aimed to assess contrasting statistical approaches for achieving accurate stem taper and individual-tree volume predictions, with a focus on the novel penalized mixed spline (PMS) approach. The approaches were tested using four different eucalyptus genetic families planted in Brazil. For comparison, the predictions of diameter outside bark (dob) and volume using a 5th-degree polynomial, the polynomial of integer and fractional powers, a segmented taper function, a variable exponent taper function, and a semi-parametric PMS were conducted. Comparisons using the generalized functions of every stem taper function with genetic family as an extra random component were also conducted. Data splitting was used to test the accuracy of each taper function. PMS and the generalized PMS were the most accurate for both dob and volume, while the generalized PMS approach displayed more stable volume predictions along the stem. The other approaches were more variable across the tree size classes examined. Overall, this study highlights the applicability of the PMS methodology for predicting tree taper and volume that warrants future assessment and application.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".