Sinuous stem growth in a Douglas-fir (<i>Pseudotsuga menziesii</i>) plantation: growth patterns and wood-quality effects
Bibliographic record
Abstract
Stem sinuosity is thought to negatively impact wood quality, but no studies have characterized its vertical and radial effects on wood properties. Here we study wood quality along the entire stem in 25-year-old plantation-grown Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) trees (32 trees total) that had been scored for sinuosity at age 12. We also study compression wood formation in the radial direction for one internode that had been scored for sinuosity at age 12 and subsequently produced 13 more annual rings. Trees with highly sinuous leaders at age 12 were more likely to be sinuous in other years, and developed more slope of grain defect (approximately 15% log volume) than less sinuous trees, but did not differ in the size of the pith-containing core. Leaders originally scored as highly sinuous developed more compression wood than control trees but only near the pith. Internode length did not differ among sinuosity classes. The size of the pith deviations (radial distance from centreline) remained constant up the stem despite a decline in internode length. However, the frequency of pith deviations was highest at 10-15 years, when internode length reached a peak. The relationship between temporal patterns of growth rate, sinuosity, and tree biomechanics deserves further attention.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".