Trends in Chemical Wood Surface Improvements and Modifications: A Review of the Last Five Years
Bibliographic record
Abstract
Increasing the use of wood in buildings is regarded by many as a key solution to tackle climate change. For this reason, a lot of research is carried out to develop new and innovative wood surface improvements and make wood more appealing through features such as increased durability, fire-retardancy, superhydrophobicity, and self-healing. However, in order to have a positive impact on the society, these surface improvements must be applied in real buildings. In this review, the last five years of research in the domain of wood surface improvements and modifications is first presented by sorting the latest innovations into different trends. Afterward, these trends are correlated to specifications representing different normative, ecologic and economic factors which must be considered when expecting to introduce a wood treatment to the market. With this review, the authors hope to help researchers to take into consideration the different factors influencing whether new innovations can leave the research laboratory or not, and thereby facilitate the introduction of new wood surface treatments in the society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".