Properties of white birch (Betula papyrifera) outer bark particleboards with reinforcement of coarse wood particles in the core layer
Bibliographic record
Abstract
$\bullet$ This study proposes substituting traditional raw materials in the surface layers of wood particleboards with the water resistant white birch (Betula papyrifera) outer bark particles, which can help improve the dimensional stability of manufactured mixed particleboards, thereby alleviating shortages of raw material in a cost-efficient manner. $\bullet$ Mixed particleboards were fabricated in the laboratory using untreated or alkali treated white birch outer bark particles as substitute material. These particles were resinated successively with three percentages of phenol-formaldehyde resin. Overall, the results of this study clearly demonstrate that the panels could be manufactured using up to 45% of the proposed substitute material and still maintain the required mechanical and physical properties. $\bullet$ Alkali treatment was used to remove natural wax from bark particles surface which hinders resin adhesion. This treatment negatively affected mechanical and physical properties of finished panels and the variation of phenol-formaldehyde resin percentage in the bark particles significantly affected only their hardness. $\bullet$ Panel with untreated bark particles in the surface layers, resinated with 5% phenol-formaldehyde resin was selected as the best with the help of a statistical analysis carried out in a factorial complete block design, especially from the dimensional stability criterion.
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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".