Bibliographic record
Abstract
Kraft pulp mills produce on average about 100 kg of solid residuals per metric ton of pulp produced. The main types of mill waste are sludge from wastewater treatment plants, ash from hog fuel boilers, dregs, grits, and lime mud from causticizing plants and lime dust from lime kilns. Of these, about half is disposed of in landfills, which highlights the need and potential for waste recycling and utilization. Sludge is either incinerated in hog fuel boilers to generate steam and power or used in various forms of land application, including land spreading, composting, or as an additive for landfill or mine waste covers. The majority of hog fuel boiler ash and causticizing plant residues is landfilled. Alkaline residuals can be conditioned for use in land application, manufacture of construction materials, and production of aggregates for road work. This technical review summarizes residuals utilization methods that have been applied in pulp and paper mills at demonstration- or full-scale, and therefore may act as a guide for mill managers and operators whose goal is to diminish the costs and the environmental impact of waste management.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".