Modeling knot geometry from branch angles in Douglas-fir (<i>Pseudotsuga menziesii</i>)
Bibliographic record
Abstract
Lumber and veneer recovery from Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) trees depend on the size and distribution of knots. Two approaches have been used to simulate the effect of knots on recovery of these products: (i) prediction of recovery based on mill studies and (ii) simulated milling of virtual trees. A benefit of the latter approach is that different milling configurations may be tested. Knots in virtual logs are usually based on data from X-ray scanning. A novel approach was used in this study to model knot geometry by inferring the development of a branch knot over time from a chronosequence of branch angle and diameter measurements. Branch angle was modeled from a database of 17 953 branch measurements on 412 trees sampled in 16 Douglas-fir plantations. Branch angles from tree tip to crown base were assumed to represent a chronosequence describing the change in branch angle. Knot pith curvature was then derived from this chronosequence of branch angles and modeled as a first-degree inverse polynomial, conditioned on tree size and position within the tree bole. Knot pith curvature was predicted to follow a linear path near the tree tip and became more curved with increasing age and depth into the crown.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".