Improvement of wood densification process via enhancing steam diffusion, distribution, and evaporation
Bibliographic record
Abstract
Mechanical densification treatments make it possible to increase the density of low- or moderate-density woods, and thus a high mechanical strength of densified wood and high-value products can be obtained. The authors’ previous treatments showed that the diffusion and distribution of steam and the release of vapor inside densified wood were prevented to some extent during thermo-hydro-mechanical (THM) densification, causing the occurrence of protrusions, carbonization, blisters, and blows. This study aimed to overcome these problems. Based on the authors’ previous THM densification, different materials, such as fabric, metal mesh, metal foam, and sintered metal mesh laminate (SMML), were used to improve the process. Densification was tested on different wood species. The results showed that SMML was the preferable material for THM densification through enhancing diffusion and distribution of steam, and evaporation of moisture inside wood. No protrusion, carbonization, blisters, or blows were found after densification with SMMLs. The densified wood specimens showed uniform color and a neat surface.
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 | 0.000 |
| 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".