Treatments to Improve the Dimensional Stability of Refractory Woods
Bibliographic record
Abstract
Changes in wood moisture content below the fiber saturation point result in dimensional changes. This creates stresses in the wood which may manifest as checks and cracks. These impact the appearance of wood products and limit the use of wood in some applications. Many chemical treatments to improve wood stability have been developed, though they are generally only applied to wood species with high permeability. The present work investigates several commercial-scale and lab-scale modification treatments for their ability to stabilize white spruce, a refractory softwood. Modified white spruce was evaluated for weight percent gain after treatment, dimensional stability in humidity and immersion, total color change after accelerated UV exposure, and coating adhesion before and after UV exposure. All treatments improved stability with anti-swelling efficiency between 11 and 59%. However, these treatments were also associated with increased color change after accelerated UV exposure and poorer adhesion of a water-based stain. The improvements in dimensional stability were generally lower than those reported for permeable species, and it’s unclear if they would meet end-user expectations. Additional research is needed to further enhance performance and to overcome the resulting photostability and coating adhesion challenges.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".