Removal of indicator bacteria, human enteric viruses, <i>Giardia</i> cysts, and <i>Cryptosporidium</i> oocysts at a large wastewater primary treatment facility
Bibliographic record
Abstract
Pathogens and fecal indicator bacteria occurrence and removal were studied for a period of 6 months at the Montreal Urban Community wastewater treatment facility. With a capacity of about 7.6 million cubic metres per day (two billion U.S. gallons per day), it is the largest primary physico-chemical treatment plant in America. The plant discharges a nondisinfected effluent containing about 20 mg/L of suspended matter and 0.5 mg/L of total phosphorus on the basis of average annual concentrations. BDO5 (annual mean) is 75 mg/L before treatment and 32 mg/L after treatment. Samples were collected for a period of 6 months, and they demonstrated that the plant was not efficient at removing indicator bacteria and the pathogens tested. Fecal coliforms were the most numerous of the indicator bacteria and their removal averaged 25%. Fecal streptococci removal was 29%, while Escherichia coli removal was 12%. In untreated sewage, fecal coliforms, E. coli, and human enteric viruses were more numerous in summer and early autumn. Fecal streptococci counts remained relatively similar throughout the period. Clostridium perfringens removal averaged 51%. Giardia cysts levels were not markedly different throughout the study period, and 76% of the cysts were removed by treatment. Cryptosporidium oocyst counts were erratic, probably due to the methods, and removal was 27%. Human enteric viruses were detected in all samples of raw and treated wastewater with no removal observed (0%). Overall, the plant did not perform well for the removal of fecal indicator bacteria, human enteric viruses, or parasite cysts. Supplementary treatment and disinfection were recommended to protect public health. Various alternatives are being evaluated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".