Impact of temperature on chlorination strategies for mussel control at water treatment plants
Bibliographic record
Abstract
Abstract Drinking water treatment plants in the Great Lakes often protect their intake structures against dreissenid biofouling by prechlorinating when water temperatures exceed 12 °C. This temperature threshold is based on the reproduction characteristics of zebra mussels. However, in recent years, zebra mussels have largely given way to quagga mussels in the region. These mussels reportedly reproduce at temperatures as low as 5 °C. The objective of this study was to determine if the current 12 °C trigger point for prechlorination remains appropriate. A 3-year monitoring program using bioboxes recorded mussel veliger concentrations and settlement potential in water drawn from the intakes of three drinking water treatment plants on Lake Ontario. Water temperature was a poor predictor of veliger presence and settlement. Reproduction and settlement were observed outside of the traditional temperature thresholds. Furthermore, no relationship was found between the number of veligers in the water column and those settling, suggesting that there are complex environmental factors that influence mussel activity. Nevertheless, it was observed that settlement occurred consistently between the months of July and November in the 3 years of the study. Therefore, a calendar-based approach to trigger prechlorination, as opposed to a temperature-based approach, is suggested.
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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 | 0.001 |
| 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".