Engaging with the water sector for public health benefits: waterborne pathogens and diseases in developed countries
Bibliographic record
Abstract
An editorial published in the Bulletin of the World Health Organization in 2008 argued for stronger engagement between the health and water sectors, commenting “a public health perspective in water management provides opportunities to both improve population health and reduce costs.” When viewed from a public health perspective, water is typically considered in terms of drinking, bathing and waste disposal but other activities, particularly food production, inshore fisheries and recreation, form important points of human contact. The water sector is diverse, comprising environmental sciences, engineering, the water supply industry, regulatory authorities and government policy-makers. A new level of engagement to involve the water sector in public health objectives is therefore dependent upon establishing a basis for dialogue and collaboration between these stakeholders, who bring widely differing conceptual approaches and practical concerns. In support of this aim, we present here a perspective on waterborne pathogens and diseases from a multidisciplinary expert group from the environmental science, microbiology, water industry, regulatory and health protection communities in the United Kingdom of Great Britain and Northern Ireland.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".