Relationships of density, microfibril angle, and sound velocity with stiffness and strength in mature wood of Douglas-fir
Bibliographic record
Abstract
The relative importance of density, acoustic velocity, and microfibril angle (MFA) for the prediction of stiffness (MOE) and strength (MOR) has not been well established for Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco). MOE and MOR of small clear specimens of mature wood were better predicted by density and velocity than by either variable alone (183 trees >20 years old, six specimens per tree, 1087 specimens total). Specimens sampled around the stem circumference had similar density (intraclass correlation coefficient t = 0.74) but not MOE (t = 0.40) or acoustic velocity (t = 0.32), indicating benefits from sampling several circumferential positions. For MOE, the path coefficients (β) were moderate for density and velocity. For MOR, β was only high for density. End-matched samples of one specimen per tree were analyzed with SilviScan. Simple correlations with MOE were highest for density (r = 0.67) and then acoustic velocity 2 (0.53), MFA (–0.50), earlywood MFA (–0.45), and latewood proportion (0.40). Most correlations were weaker for MOR. Density had a higher β than did MFA for either MOE or MOR. In more complex path models, latewood proportion and latewood density were the most important contributors to MOE and MOR, and MFA was relatively unimportant. The path analyses showed what simple correlation did not: that latewood proportion has strong predictive value for Douglas-fir mature wood quality.
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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 | 0.001 | 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".