Acoustic-Based Non-Destructive Estimation of Wood Quality Attributes within Standing Red Pine Trees
Bibliographic record
Abstract
The relationship between acoustic velocity (vd) and the dynamic modulus of elasticity (me), wood density (wd), microfibril angle, tracheid wall thickness (wt,), radial and tangential diameters, fibre coarseness (co) and specific surface area (sa), within standing red pine (Pinus resinosa Ait.) trees, was investigated. The data acquisition phase involved 3 basic steps: (1) random selection of 54 sample trees from 2 intensively-managed 80-year-old plantations in central Canada; (2) attainment of cardinal-based vd measurements transecting the breast-height position on each sample tree; and (3) felling, sectioning and obtaining cross-sectional samples from the first 5.3 m sawlog from which Silviscan-based area-weighted mean attribute estimates were determined. The data analysis phase consisted of applying graphical and correlation analyses to specify regression models for each of the 8 attribute-acoustic velocity relationships. Results indicated that viable relationships were obtained for me, wd, wt, co and sa based on a set of statistical measures: goodness-of-fit (42%, 14%, 45%, 27% and 43% of the variability explained, respectively), lack-of-fit (unbiasedness) and predictive precision (±12%, ±8%, ±7%, ±8% and ±6% error tolerance intervals, respectively). Non-destructive approaches for estimating the prerequisite wd value when deploying the analytical framework were also empirically evaluated. Collectively, the proposed approach and associated results provide the foundation for the development of a comprehensive and precise end-product segregation strategy for use in red pine management.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".