Estimation of individual knot volumes by mixed-effects modelling
Bibliographic record
Abstract
We present a new method to estimate individual knot volumes based on a knot geometry model coupled with observations on branch characteristics. X-ray computer tomography and image analysis were used to measure the volume and geometry of 424 knots of Sitka spruce (Picea sitchensis (Bong.) Carrière). Knot geometry can be described mathematically by deriving functions for relative vertical position, diameter, and slope dependent on radial position in the stem. These functions were parameterized using “seemingly unrelated regression” and mixed-modelling techniques. This provided a base model for typical knots. To estimate individual knot volume, we used available data for branch diameter and insertion angle to obtain conditional predictions. We imputed the most likely knot trajectory, as relative vertical position cannot be measured on branches. The model explained up to 96% of the variability in knot volume by incorporating the branch measurements, in contrast to the 43% explained using the typical knot model. Knot volume assessment based only on conditional predictions of diameter and marginal predictions of vertical position also accounted for 96% of the variability. Therefore, measurements of branch diameter alone would be enough to obtain highly precise predictions of individual knot volume. This estimator is a first step towards a knot model to be used for the management of Sitka spruce in Great Britain.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".