DEVELOPMENT AND EVALUATION OF ADVANCED MOLECULAR STRATEGIES FOR QUALITY MONITORING AND SUSTAINABILITY OF RECREATIONAL WATERS
Bibliographic record
Abstract
Traditional culturing-based enumeration methods for qualitative monitoring of freshwater beaches provide a delayed assessment of microbial risk factors, and the resulting derived information obtained is limited to fecal indicator counts. Delays in acquiring fecal indicator counts can lead to error-prone beach postings, and the acquired information does not identify additional microbial factors or specific fecal contamination sources. However, the introduction of novel DNA sequencing methodologies and new forms of bioinformatics analyses has revolutionary potential to augment conventional water monitoring technologies. In a series of studies on Southern Ontario recreational waters, including the Great Lakes, we have explored the use of these DNA-based technologies, including rapid qPCR-based assays, DNA sequencing, use of conserved Signature Genes/Proteins and environmental DNA (eDNA) metabarcoding to improve public health responses. This thesis includes evaluating qPCR methods for routine quality assessment of freshwater beaches, demonstrating the capability for rapid, accurate monitoring and timely decision-making. To address conventional fecal contamination monitoring limitations, a novel E. coli-specific qPCR strategy based on Conserved Signature Proteins (CSPs) was also developed as an alternative to conventional microbial markers, offering greater specificity and reduced false positives/negatives. The use of DNA sequencing (metagenomic) analysis revealed microbial community changes associated with fecal indicator exceedances, uncovering cyanobacteria, cyanotoxins, and antibiotic-resistance genes not detected by traditional bacterial culturing methods. eDNA metabarcoding was evaluated to identify a broad spectrum of fecal contamination components and characterize region-specific differences. Identifying the root causes of water quality deterioration using fecal source tracking can enable targeted interventions and a deeper understanding of recreational water quality changes. This thesis underscores the potential of adopting advanced molecular techniques for comprehensive microbial risk assessment and sustainable management of recreational water ecosystems, ultimately improving water quality monitoring and ensuring safer recreational environments and better public health outcomes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".