Same-day Enterococcus qPCR results of recreational water quality at two Toronto beaches provide added public health protection and reduced beach days lost
Bibliographic record
Abstract
OBJECTIVES: We evaluated the potential impacts from using a rapid same-day quantitative polymerase chain reaction (qPCR) monitoring method for beach posting outcomes at two Toronto beaches. METHODS: In total, 228 water samples were collected at Marie Curtis Park East and Sunnyside Beaches over the 2021 summer season. Water samples were processed using the USEPA 1609.1 Enterococcus qPCR-based method. Escherichia coli (E. coli) culture data and daily beach posting decisions were obtained from Toronto Public Health. RESULTS: No significant correlation was observed between previous-day and same-day (retrospective) E. coli enumeration results at any Sunnyside Beach transect, and only relatively low (R = 0.41-0.56) or no significant correlation was observed at sampling transects for Marie Curtis Park East Beach. Comparing our same-day Enterococcus qPCR data to Toronto's 2-day E. coli geometric mean beach posting decisions, we noted the need for additional postings for 1 (2%) and 3 (8%) missed health-risk days at Sunnyside and Marie Curtis Park East Beaches, respectively. The qPCR data also pointed to incorrect postings for 12 (31%) and 6 (16%) lost beach days at Sunnyside and Marie Curtis Park East Beaches, respectively. CONCLUSION: Application of a rapid Enterococcus qPCR method at two Toronto beaches revealed 5% of beach posting decisions were false negatives that missed health-risk days, while 23% of decisions were false positives resulting in lost beach days. Deployment of the rapid same-day qPCR method offers the potential to reduce both health risks and unnecessary beach postings.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".