Bibliographic record
Abstract
Trapped sediment has robbed roughly 50,000 large dams worldwide of an estimated 13% to 19% of their combined original storage capacity, and total losses will reach 23% to 28% by 2050, according to United Nations (UN) research.The global loss from original dam capacity foreseen by mid-century-from ~6,300 billion to ~4,650 billion m 3 in 2050, a difference of ~1,650 billion m 3roughly equals the annual water use of India, China, Indonesia, France, and Canada combined.The United Nations University's (UNU's) Canadian-based Institute for Water, Environment and Health (INWEH) applied previously determined storage loss rates in various areas worldwide to large dams in 150 countries to forecast cumulative reservoir storage losses by country and region as well as globally.The study shows that the United Kingdom, Panama, Ireland, Japan, and Seychelles will experience the highest water storage losses by 2050-between 35% and 50% of their original capacities.By contrast, Bhutan, Cambodia, Ethiopia, Guinea, and Niger will be the five least-affected countries, losing less than 15% by mid-century.Duminda Perera coauthored the study, "Present and Future Losses of Storage in Large Reservoirs Due to
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".