A comparative analysis of current microbial water quality risk assessment and management practices in British Columbia and Ontario, Canada
Bibliographic record
Abstract
Bacteria, protozoa and viruses are ubiquitous in aquatic environments and may pose threats to water quality for both human and ecosystem health. Microbial risk assessment and management in the water sector is a focus of governmental regulation and scientific inquiry; however, stark gaps remain in their application and interpretation. This paper evaluates how water managers practice microbial risk assessment and management in two Canadian provinces (BC and Ontario). We assess three types of entities engaged in water management along the source-to-tap spectrum (watershed agencies, water utilities, and public health authorities). We analyze and compare the approaches used by these agencies to assess and manage microbial risk (including scope, frequency, and tools). We evaluate key similarities and differences, and situate them with respect to international best practices derived from literatures related to microbial risk assessment and management. We find considerable variability in microbial risk assessment frameworks and management tools in that approaches 1) vary between provinces; 2) vary within provinces and between similar types of agencies; 3) have limited focus on microbial risk assessment for ecosystem health and 4) diverge considerably from the literature on best practices. We find that risk assessments that are formalized, routine and applied system-wide (i.e. from source-to-tap) are limited. We identify key limitations of current testing methodologies and looking forward consider the outcomes of this research within the context of new developments in microbial water quality monitoring such as tests derived from genomics and metagenomics based research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".