Why is financing water resources management an issue?
Bibliographic record
Abstract
As societies made progress overtime in securing access to water, the subject progressively slipped away from the public agenda, at least in OECD countries. In the second half of the 20th century, rapid demographic and economic growth put increasing pressure on the water resource, both in terms of quantity and quality. As a response, many OECD countries have made significant efforts in the last three decades to clean up rivers – mostly by treating wastewater from urban and industrial centres. Water scarcity has always commanded attention in more arid countries, like Spain and Mexico, but countries that once perceived themselves as water-rich – such as Canada, New Zealand or the United Kingdom – are progressively realising their increasing vulnerability as population and economic growth takes place in areas with relatively low rainfall, where there is currently limited water storage capacity, and exposed to changing hydrological patterns. Managing “too much water” is also a major concern – indeed, flood management is highlighted in most recent Environmental Performance Reviews of OECD member countries.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".