Effect of thinning on relationships between three measures of wood stiffness in <i>Pinus radiata</i>: standing trees vs. logs vs. short clear specimens
Bibliographic record
Abstract
The effect of thinning on the relationship of wood quality traits measured on standing trees (dynamic modulus of elasticity (MOE) and outerwood density) and traits measured on logs or short clear specimens was determined using data collected from radiata pine ( Pinus radiata D. Don) trees growing in 22 unthinned and 16 thinned plots of harvest age trees in New South Wales, Australia. Stiffness showed a linear decrease along the stem. Trees growing on thinned sites were, on average, 3% lower in stiffness at each height in the stem. MOE measured on short clear specimens was moderately related to standing tree MOE (R2 = 0.62) and outerwood density (R2 = 0.56) but less well related to MOE of the adjacent log (R2 = 0.30). Standing tree MOE was a better predictor of whole stem MOE for the thinned sites (R2 = 0.60) than for the unthinned sites (R2 = 0.31). Stiffness and density appear to follow different patterns of variation and results for density may not be extrapolated to stiffness. Outerwood density was a very poor predictor of mean whole stem stiffness (R2 = 0.14). Overall, the acoustic tool, TreeTap, was a better predictor of whole stem stiffness than outerwood density, particularly for the thinned sites.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".