Review of works on Walkerton, Ontario water contamination
Bibliographic record
Abstract
As Canadians, we changed after May, 2000 when the unexpected happened.Now, Walkerton, Ontario means death and illness caused by negligence and criminality within our public drinking water system.Walkerton's water was contaminated with cattle manure containing a virulent strain of E. coli bacteria.The manure didn't enter the town's wells because of an irresponsible farmer.It entered Walkerton's water supply, undetected until sickness and death, because those responsible for the city's water, Stan and Frank Koebel, didn't understood the risk of E. coli contamination.They didn't sample water correctly and they ignored results showing astronomical E. coli counts.Why did the Koebels, two high school drop-outs, hold responsibility for the safety of Walkerton's water?Who checked Stan and Frank's work?Where were the checks and balances provided by a concerned public, city council, the utilities commission, the water testing laboratory, the regional health office, the provincial Ministry of the Environment and Health Canada?Politicians, bureaucrats and engineers who design drinking water systems like to explain that a multi-barrier approach is taken to protect public drinking water.Even if one barrier is breached there are back-ups to protect public health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".