Co-composting of Paper Mill Sludge and Hardwood Sawdust Under Two Types of In-Vessel Processes
Bibliographic record
Abstract
Recycling of organic residues by composting is becoming an acceptable practice in our society. Co-composting dewatered paper mill sludge (PMS) and hardwood sawdust, two readily available materials in Canada, was investigated using uncontrolled and controlled in-vessel processes. The composted materials were characterized for total C and N, water-soluble, acid-hydrolyzable, and non-hydrolyzable N, extractable lipids, and by Fourier Transform Infrared (FT-IR) spectrophotometry. In the controlled scale process, the loss of organic matter was approximately 65% higher than in the uncontrolled process. After undergoing initial fluctuations in N fractions during the first two days of composting, by the end of the process, concentrations of water-soluble N decreased while those of acid-hydrolyzable and nonhydrolyzable N increased in the controlled process, whereas in the uncontrolled process, water-soluble N increased, but N in the other two fractions decreased continuously, indicating that the biochemical transformations of organic matter were not completed. Data on extractable lipids and FT-IR spectra suggest that the compost produced from the controlled process was bio-stable after 14 days, while the uncontrolled process was not stabilized after 18 days. In addition, FT-IR data suggest the biological activity during composting centered mainly on the degradation of aliphatic structures while aromatic structures were preserved. The co-composting of the PMS and hardwood sawdust can be successfully achieved if aeration, moisture, and bio available C/N ratios are optimized to reduce losses of N.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".