Effects of Hot-Water Treatment of Black Spruce and Trembling Aspen Bark RAW Material on the Physical and Mechanical Properties of Bark Particleboard
Bibliographic record
Abstract
The understanding of the interaction between bark extractives and adhesives is fundamental in the manufacture of bark particleboard for optimum adhesive curing, and mechanical and physical properties of the boards. The effect of hot-water treatment on black spruce and trembling aspen bark was investigated to highlight its impact on the bark particles/phenol-formaldehyde adhesive system, and on the physical and mechanical properties of bark particleboard made from hot-water-treated bark of both species. Bark was soaked in hot water maintained at 100°C for 3 h. The results showed that the hot-water treatment affects the physical and chemical properties of the bark by decreasing hydrophilic characteristics, acidity, and the amount of condensable polyphenols that can react with formaldehyde. The mechanical properties, including static bending and internal bond of particleboard made from untreated black spruce and trembling aspen bark, were higher than those of boards made from hot-water-treated bark of the same species. The thickness swelling of particleboard made from hot-water-treated black spruce and trembling aspen bark was higher than that made from untreated bark. One exception occurred for particleboard made from 100% trembling aspen bark for which no significant difference was found between particleboards made from treated and untreated barks.
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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 | 0.000 | 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".