Wood coloring by reactive stains
Bibliographic record
Abstract
The appearance of interior wood products (e.g.: furniture and floors) is often the first criteria that affects customer’s interests while making a purchase. One way to diversify the colors and appearances of wood products is by using reactive stains. These coloring systems, consisting of aqueous solutions of metal salts, can penetrate wood and react with its phenolic compounds by forming metal complexes. The color of wood obtained depends on the type of phenolic compounds, type of metal salt, wood surface preparation, temperature, wood humidity, and others. In order to determine the relationship between the structural characteristics of the phenolic compounds and the color developed on wood surface, the polyphenols of two North American hardwood species were extracted and analyzed by different spectrophotometric methods and by liquid state phosphorus-31 nuclear magnetic resonance (NMR) spectroscopy. The chromatic coordinates (CIELAB system) of wood colors obtained after application of reactive stains were compared for these hardwood species. A better knowledge of the reaction mechanisms and the factors influencing them, will allow the optimal use of these systems in wood finishing industries. Since the colored products are present in the wood structure, the wood grain appearance will be preserved or even be accentuated. Enhancing the natural and warm aspect of wood used in buildings interiors can contribute to the well-being of the consumers and promote furthermore the use of this biosourced material.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".