Human health impacts of dams and reservoirs: Neglected issues in a One Health perspective
Bibliographic record
Abstract
Abstract Dams have often been constructed for hydropower, water storage and to support socio-economic development, particularly in areas of water stress. In many places, the water stored in human-made reservoirs is essential to meet the development objectives of water supply, agriculture, industry, energy generation and other sectors. However, in the absence of adequate foresight and planning, many past dams have had considerable negative impacts on ecosystems and the livelihoods of affected communities, resulting in conflicts and health hazards. While enhanced human health and well-being could be considered as the ultimate outcome of development programs, the public health impact of dams remains an issue that is often neglected by policy makers and investors. National policies and international guidelines, such as those of the World Commission on Dams, have been used to improve planning and impact assessment of dams. Here, we provide an analysis of four large dams, across three continents, and show that they had limited consistency with World Commission on Dams principles and guidelines. Moreover, health aspects were largely neglected during planning, construction and operation of these dams, but seriously undermine their intended benefits. This perspective paper discusses impacts of dams on energy and food, ecosystem health, inclusion, and ultimately human health and wellbeing. We argue that a One Health perspective, based on these four categories, can support the systematic consideration of environmental, animal, and human health determinants. A dedicated One Health approach to dams and reservoirs remains to be developed but could potentially improve how dams, both existing and future, support more inclusive development.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".