Toxicological properties of a major release of untreated wastewaters into the St. Lawrence River to quagga mussels Dreissena bugensis
Bibliographic record
Abstract
Before maintenance work could be carried out on the sewage system in the city of Montreal (Quebec, Canada), 5 billion litres of untreated wastewater had to be released directly into the St. Lawrence River over a five-day period in November 2015. The purpose of this study was to examine the toxicity of untreated wastewaters on quagga mussels. Water samples were collected at various points from the most densely populated downtown area and downstream points: 0 km, 5 km, 12 km, 16 km and 19 km. A river water sample was collected at the opposite shore as a reference site, and aquarium (dechlorinated tap) water was used for controls. Mussels were exposed for four days at 15 oC to these wastewaters, then examined for biotransformation (CYP1A1 and glutathione S-transferase activities), energy expenses (mitochondria activity and triglycerides), estrogenicity (alkali-labile phosphates) and damage (lipid peroxidation and DNA strand breaks). The data revealed that exposure to the released wastewaters produced changes to all the above biomarkers. CYP1A1 activity, DNA damage and triglyceride levels were the most responsive biomarkers for optimal site classification as determined by discriminant function analysis. CYP1A1 activity, lipid peroxidation and alkali-labile phosphate levels were significantly correlated with distance from downtown, suggesting that population density influenced more directly those effects. In conclusion, exposure to untreated wastewaters could lead to adverse toxic effects in quagga mussels. Mussels could be at risk from the release of untreated wastewaters events given that high-intensity precipitation could also result in the release of untreated wastewaters in these times of climate change.
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".