CHARACTERISTICS OF PAPER MILL SLUDGE AND ITS UTILIZATION FOR THE MANUFACTURE OF MEDIUM DENSITY FIBERBOARD
Bibliographic record
Abstract
De-inking paper sludge (DPS) and primary sludge (PS) containing 20% secondary sludge from a paper mill were characterized as to their suitability for the manufacture of medium density fiberboard. Compared with DPS, PS had a lower ash content, higher holocellulose content, more and longer fibers, lower pH, and higher buffering capacity. These characteristics make PS a better fiber resource for fiberboard than DPS. Fiberboards were manufactured at the Pilot Plant of Forintek (Québec City, QC, Canada) using virgin spruce-pine-fir fiber (SPF) and PS or DPS at different sludge/SPF weight ratios with 12% ureaformaldehyde resin. At an equal sludge/SPF weight ratio, PS-SPF panels had much higher mechanical properties than did DPS-SPF panels. At a PS/SPF weight ratio of 7:3, the mechanical properties of PS-SPF panels were higher than the requirements of ANSI A208.2-2002 MDF standard for Grade 120 in terms of internal bond strength, modulus of rupture, modulus of elasticity, and thickness swelling. With DPS/SPF weight ratios as low as 3:7, the tested mechanical properties of DPS-SPF panels could meet the requirements of ANSI A208.2-2002 MDF standard for Grade 120.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".