Occurrence of Waterborne Pathogens and Escherichia coli at Offshore Drinking Water Intakes in Lake Ontario
Bibliographic record
Abstract
The occurrence of waterborne pathogens was investigated at three drinking water intakes located about 2 km offshore in Lake Ontario. Water sampling was conducted over 3 years for Campylobacter spp., Cryptosporidium spp., Giardia spp., cultivable enteric viruses, and water quality parameters. All pathogens were detected in the offshore source water for each water treatment plant (WTP1 to WTP3), although at relatively low frequencies and concentrations. Giardia was the most common pathogen, occurring in 36% of water samples from the influent of WTP1 (n = 46), and with a maximum concentration of 0.70 cysts/liter in this influent. Cryptosporidium occurred as frequently as 15% in the WTP2 influent (n = 35), with a maximum concentration of 0.40 oocysts/liter in the WTP1 influent. The human Bacteroidales HF183 DNA marker was most common in the WTP1 influent (19%), and this was the only WTP where the Cryptosporidium hominis genotype was detected. No water quality parameter was predictive of pathogen occurrence across all three WTP influents. Escherichia coli was often below detection when pathogens were detected, and spikes in E. coli concentrations often did not coincide with pathogen occurrence. After summer rain events, river plumes had E. coli concentrations as high as 222 CFU/100 ml in surface waters 2 km offshore, without impacting drinking water intakes below the thermocline on the lake bottom. At times, prechlorination to control mussels at offshore intake cribs compromised the use of E. coli for "raw" water quality assessment, particularly for chlorine-resistant Cryptosporidium. E. coli measured by standard methods did not reliably predict pathogen occurrence at drinking water intakes in offshore ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".