Acoustic-Based Prediction of End-Product-Based Fibre Determinates within Standing Jack Pine Trees
Bibliographic record
Abstract
The objective of this study was to specify, parameterize, and evaluate an acoustic-based inferential framework for estimating commercially-relevant wood attributes within standing jack pine (Pinus banksiana Lamb) trees. The analytical framework consisted of a suite of models for predicting the dynamic modulus of elasticity (me), microfibril angle (ma), oven-dried wood density (wd), tracheid wall thickness (wt), radial and tangential tracheid diameters (dr and dt, respectively), fibre coarseness (co), and specific surface area (sa), from dilatational stress wave velocity (vd). Data acquisition consisted of (1) in-forest collection of acoustic velocity measurements on 61 sample trees situated within 10 variable-sized plots that were established in four mature jack pine stands situated in boreal Canada followed by the removal of breast-height cross-sectional disk samples, and (2) given (1), in-laboratory extraction of radial-based transverse xylem samples from the 61 disks and subsequent attribute determination via Silviscan-3. Statistically, attribute-specific acoustic prediction models were specified, parameterized, and, subsequently, evaluated on their goodness-of-fit, lack-of-fit, and predictive ability. The results indicated that significant (p ≤ 0.05) and unbiased relationships could be established for all attributes but dt. The models explained 71%, 66%, 61%, 42%, 30%, 19%, and 13% of the variation in me, wt, sa, co, wd, ma, and dr, respectively. Simulated model performance when deploying an acoustic-based wood density estimate indicated that the expected magnitude of the error arising from predicting dt, co, sa, wt, me, and ma prediction would be in the order of ±8%, ±12%, ±12%, ±13%, ±20%, and ±39% of their true values, respectively. Assessment of the utility of predicting the prerequisite wd estimate using micro-drill resistance measures revealed that the amplitude-based wd estimate was inconsequentially more precise than that obtained from vd (≈ <2%). A discourse regarding the potential utility and limitations of the acoustic-based computational suite for forecasting jack pine end-product potential was also articulated.
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".