Evaluation of Mussel Control Strategies in Drinking Water Treatment Plants: Peracetic Acid, Earthtec QZ, and Preclorination
Bibliographic record
Abstract
Mussel macrofouling is a major concern for water treatment plants in the Great Lakes region. This research evaluated peracetic acid (PAA) and EarthTec QZ for mussel control by conducting batch and flow-through tests. Peracetic acid doses ≥ 5 mg/L and a dose of 60 μg/L EarthTec QZ were effective for adult mussel control. This research also explored potential impacts of applying PAA for mussel control on chlorine disinfection by developing a mathematical model running multiple simulations at different pHs. It was concluded that chlorine consumes the hydrogen peroxide in a few minutes, whereas PAA and AA concentrations were largely unaffected. A final component of the research was a multi-year monitoring program at two plants in Ontario to explore settlement rates as a function of temperature. The evidence suggested that temperature alone is not a reliable indicator of settlement activity, and that a calendar-based prechlorination program may be more effective.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".