Removal of individual sterols during secondary treatment of pulp mill effluents
Bibliographic record
Abstract
Analysis of plant sterols in pulp mill effluents is complex and several investigations have combined wood extractives with sterols or have reported total sterols collectively. The present study exclusively focuses on the fate of individual sterols generated in pulp and paper manufacturing as they pass through activated sludge biotreatment system. Before secondary treatment the pulp mill effluents contained 33% ß-sitosterol, 26% campesterol, 22% ß-sitostanol, 17% stigmasterol and 2% ergosterol (1,000–1,800 μg/L mean total sterols). After treatment, the effluents contained 44% ß-sitosterol, 22% campesterol, 18% ß-sitostanol, 14% stigmasterol and 2% ergosterol (176–428 μg/L mean total sterols). Each sterol fraction showed different removal efficiency. ß-Sitosterol, the major fraction, was removed relatively poorly (65%) while campesterol was removed most efficiently (81%) compared with the removal of other fractions (ß-sitostanol 74% and stigmasterol 64%). The differential removal of sterol fractions altered the sterol profile during different stages of the secondary treatment. Owing to its poor removal, ß-sitosterol is the most persistent fraction in treated/untreated pulp mill effluents. Typically, 21% of the incoming sterols were contained in secondary effluents and 23% in waste sludge without biodegradation. Optimizing the design/operation of treatment systems for removal of ß-sitosterol and stigmasterol would improve the performance of effluent treatment facilities.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".