Comparative microbial source tracking methods for identification of fecal contamination sources at Sunnyside Beach in the Toronto region area of concern
Bibliographic record
Abstract
Several beaches within the Toronto region area of concern have persistent issues with fecal contamination, causing a beach beneficial use impairment (BUI). In this study, Escherichia coli, including ampicillin-resistant strains, were enumerated via culturable and quantitative polymerase chain reaction (qPCR) methods. Microbial source tracking (MST) markers (for general Bacteroidales, human, ruminant/cow, gull, and dog) were detected and enumerated via PCR and qPCR to identify sources of fecal contamination at Sunnyside Beach and in the Humber River. Human, cow, and dog markers had good host-specificity, while gull markers sometimes amplified a few other bird species. The ruminant endpoint PCR marker amplified a variety of other animal species rendering it less useful. Both human and gull fecal contamination were prevalent in the Humber River, while Sunnyside Beach was predominantly impacted by gull fecal contamination. Human sewage impacts were more prevalent in the lower Humber River, particularly in Black Creek. However, to reduce Sunnyside beach postings, reducing bird fecal contamination in the river and at the beach would be necessary. When there are high levels of E. coli throughout a beachshed, an MST toolbox approach can add value to discriminate source(s) of E. coli contamination and guide decisions relating to public health risk and remediation strategies.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".