Acoustic velocity at the tree, log, and lumber levels and their relationship with lumber bending properties
Bibliographic record
Abstract
This study investigated the relationships between acoustic velocity (AV) measured on standing trees, stems, logs, and lumber pieces, and static lumber bending stiffness (modulus of elasticity (MOE)) and strength (modulus of rupture (MOR)). The relationships were examined at four measurement scales: i.e., tree, stem, log, and lumber level. The impacts of tree and log characteristics and wood properties on model performance were also examined. The strongest relationship among AVs was found between log AV and stem AV at the tree ( R2 = 0.877) and log levels ( R2 = 0.454), and between lumber AV and log AV at the lumber level ( R2 = 0.125). No or weak relationships were found between lumber AV and tree/stem AVs and between bending properties and tree AV at all levels. Lumber MOE and MOR had the strongest relationships with lumber AV at the tree and log levels ( R2 = 0.478–0.641). Stem AV and log AV had a similar impact on MOE and MOR within each level ( R2 = 0.05–0.48). Diameter at breast height, crown width, and wood density were the most common covariates that contributed most to the variances explained for AV, MOE, and MOR at the tree and lumber levels, except for the impact of crown width on MOR. At the log level, log position in the stem and green density were the most important contributing factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".