Effects of thermal modification on mechanical and swelling properties and color change of lumber killed by mountain pine beetle
Bibliographic record
Abstract
To extend the application of mountain pine beetle (MPB) killed lumber for decking, siding, and landscaping materials, it is essential to improve its dimensional stability. Thermal treatment is one of the well-established processes used to improve wood stability by modifying chemical compounds and masking blue-stains by darkening the fibre color. In this study, the MPB lumber was subjected to thermal treatment at three temperatures (195, 205, or 215°C) and three exposure times (1.5, 2, or 3 h). Based on Duncan’s multiple range test, the results indicated that the volumetric swelling after thermal treatment, either from oven-dry to air-conditioned or from oven-dry to water-saturated, was significantly reduced after thermal treatment. Modulus of elasticity was increased when specimens were treated at a temperature of 195°C, and then decreased as the temperature increased. Modulus of rupture was significantly reduced as treatment temperature increased. The hardness of lumber thermal-treated at 195°C was significantly increased compared to that of the untreated lumber. At higher temperatures, hardness started to decrease slightly. With the treatment temperature increasing to 215°C for 3 h, the color difference between stained and clear wood was reduced by 75%. As a result, the blue-stains vanished gradually.
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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 | 0.000 |
| 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) | 0.001 | 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".