Optimum Research of Hot-Pressing Technology of the Composite Board with Waste Wood and Paper
Bibliographic record
Abstract
On the rise of global low-carbon economy with the purpose of leveraging waste wood resources efficiently, waste wood and paper was processed into new type wood-based panels. The hot-pressing technology of the 9 mm composite board with waste wood and paper was studied through an orthogonal experiment, and the effects of resin content, hot-pressing temperature, hot-pressing time and the mass ratio of waste wood and paper were discussed. Results indicated that effects of resin content on the MOR (Modulus of rupture), MOE (modulus of elasticity) and 24h TS (thickness swelling) of the composite board were remarkable, while effects of hot-pressing temperature, hot-pressing time and the mass ratio of waste wood and paper were slight. Based on the quality indicators of MOR, it was found that the optimum condition of hot-pressing for the 9 mm composite board with waste wood and paper was estimated to include resin content of 19%, hot-pressing temperature of 120 °C, hot-pressing time of 12.5min, and the mass ratio of waste wood and paper of 7 to 13. Under these conditions, the composite board could be used as new materials for furniture, interior decoration and packaging. Key words: Waste wood; Waste paper; Composite board; Hot-pressing technology
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".