Predicting wood stiffness of lodgepole pine trees using acoustic tools and green density
Bibliographic record
Abstract
Upstream identification of wood properties using non-destructive testing methods such as acoustic velocity (AV) measurements is important for optimizing allocation of wood to mills or products. We evaluated the effectiveness of field AV measurement tools in predicting lodgepole pine wood stiffness (modulus of elasticity, MOE) as measured by Silviscan on wood samples. AV was measured on trees and logs from six sites in Alberta and British Columbia. We evaluated the effect on MOE estimation of calculating averages of the adjustment factor k and of green density (GD) at different spatial scales from individual tree to population. The effect of using forest inventory variables on MOE prediction were also examined. Prediction of tree-level MOE from tree-level measurements of AV, k and GD resulted in R2 values of 0.59. Using estimates of k and GD averaged at plot, site or population scales significantly diminished the R2 of the MOE predictions at tree level. Predicting MOE at plot or stand level from corresponding averages of AV, k and GD gave R2 values >0.8. Including inventory variables in tree-level MOE predictions increased the R2 to 0.62. AV measurements can give operationally useful estimates of MOE in lodgepole pine trees at the stand level.
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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 | 0.000 |
| 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".