Experimental campaign on the mechanical properties of Canadian small clear spruce-pine-fir wood: Experimental procedures, data curation, and data description
Bibliographic record
Abstract
This article contains an experimental dataset related to the mechanical properties of Canadian small clear spruce-pine-fir wood. Motivated by the necessity of shading light on the orthotropic mechanical behavior of clear specimens of two of the most common wooden grades used for the manufacturing of cross-laminated timber panels in North America, a comprehensive experimental campaign on small clear spruce-pine-fir wood specimens, based on ASTM D143-22, has been conducted in the Department of Wood Science of the University of British Columbia. A total of 690 specimens from both visually-graded number 2 and machine-stress rated 2100fb 1.8E spruce-pine-fir wood were tested in compression, tension, and shear, following the directions parallel- and perpendicular-to-the-grain. For each test, the force and the deformation were recorded on-line through an MTS software before being stored in a hard drive disk unit as text files at the end of the test. Text files were then post-processed using a MATLAB routine to generate stress-strain data points, ultimate strength, and modulus of elasticity. Additionally, probability distributions of ultimate strength and modulus of elasticity of specimens were plotted. A Kolmogorov-Smirnov goodness-of-fit test was used to fit these data using either Burr, Gumbel, or Weibull distribution. In overall, the dataset presented in this work can be used in the finite-elements modelling of the structural behavior of timber connections or the local mechanical behavior of timber elements. This dataset can also be used to get a grasp and asses the variability in the mechanical properties of Canadian small clear spruce-pine-fir wood.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".