Strength grading of northern hardwood species for structural engineered wood products: Identification of the relevant indicating properties
Bibliographic record
Abstract
Strength grading of hardwoods is a prerequisite to use them in structural engineered wood products. However, hardwood strength grading is considerably less developed than it is for softwood species. Previous study has shown that white ash and yellow birch are promising species for the manufacture of glued-laminated timber. However, no strength grading procedure dedicated to hardwoods is available in Canada. The objective of this study was to identify the relevant indicating properties for predicting the ultimate tensile strength of the investigated species. A model selection approach allowed to identify the most performing models and to compare, for each species, the relative impact of the indicating properties. The indicating properties included in the final models were the density of the specimens, the dynamic modulus of elasticity, the sinus of the maximum local grain deviation (SGDmax) as well as the knot area index (KAI), derived from the knot area ratio. The final models revealed important differences between the two species, indicating that it may be relevant to grade them separately to ensure the most efficient utilization of the resource. The coefficients of determination between the actual and model predicted UTS were 0.82 for white ash and 0.78 for yellow birch.
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".