The Toledo Drinking Water Advisory: Suggested Application of the Water Safety Planning Approach
Bibliographic record
Abstract
On 2 August 2014 the city of Toledo, in Ohio USA issued a “do not drink” water advisory and declared a state of emergency. This was as a result of elevated levels of the toxin microcystin in the final treated water, a dangerous toxin produced by the algae cyanobacteria. The Toledo water crisis is a key focusing event that can advance dialogue on eutrophication governance in the context of public health. This paper examines the Toledo water ban with the aim of determining whether this crisis could have been averted. Further, we explore how this event can be used to stimulate action on eutrophication governance, to motivate action to protect water at its source. We use the World Health Organization’s Water Safety Planning Methodology to show that the crisis could have been averted with some simple risk management actions. We also show that a water safety planning approach could lead to well developed operational and maintenance planning resulting in a higher probability of safe drinking water.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".