Characterization of recycled paper mill sludge and evaluation of potential applications
Bibliographic record
Abstract
This research addresses re-use of recycled paper mill sludge (RPS), a waste by-product of recycled paper manufacturing. The growth of the paper recycling industry over the past 15 years has led to ecological benefits; however it is estimated that over 9 million tonnes of sludge by-product are produced each year in North America, with limited use in beneficial applications. Recycled paper mill sludges that represent a significant segment of the recycling industry were used in this work. Sludges were prepared using a novel kinetic de-watering system that dried and fiberized the material. The unique constitution of the dry sludge overcame constraints of failed utilization attempts of the past. Comprehensive physical and chemical characterization elucidated differences in the sludges based on generating process and raw furnish. Differences in organic content, fibre length, inorganic content and fibre chemistry were found to be strongly influenced by the recycling processes. Fibre contained in recycled newsprint manufacturing sludge had characteristics similar to TMP/mechanical fibre, while the fibre contained in recycled tissue manufacturing sludge were more similar to chemically prepared fibre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".