Modeling Effects of Incising on Flexural Properties of Green Douglas Fir and Western Hemlock Lumber
Bibliographic record
Abstract
ABSTRACT Incising is required in Canadian and US national standards for the treatment of many timber species, especially those from western North America. Incising increases the amount of end-grain exposed to fluid flow, but it also decreases the cross-sectional area and, thus, has the potential to affect mechanical properties. Models have been developed to predict the effects of incising on flexural properties, but correlative data for engineering design adjustment factor(s) for incised and preservative treated lumber and timber are lacking. Current engineering design adjustment factors are primarily based on nominal 2x (38-mm, 1.5-in)-thick lumber tests. In 2018, this fact was recognized and the new design standard allowed for theoretical models to be used for larger materials where appropriate test data were not currently available. This article reports the results of mechanical tests evaluating the effects of incising on flexural properties of nominal 3x and 4x (64- and 89-mm)-thick by 184-mm (7.25-in)-wide Douglas fir and western hemlock lumber. It uses these data to develop potential engineering design adjustment factor(s) for incised nominal 2x (64- and 89-mm)-thick lumber and discusses theoretical modeling to predict those factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".