Bibliographic record
Abstract
Water security is a major challenge for science and society. We review the rapidly growing literature on water security from the perspective of risk science and management. Competing definitions and indicators of water security reflect unsettled conceptual and methodological issues. However, risk concepts have become prevalent in defining water security; measuring it quantitatively; tracking indicators of hazards, exposure, and vulnerability; and informing management options to reduce water-related risks. We examine water security indicators and indices to identify thresholds for water-related risks across multiple dimensions of water security and examine how these vary across different scales and socioeconomic contexts. Water security indicators reveal a disparity in hazards and vulnerability across geographic and political-economic conditions. Recognition of water security as a major societal challenge has been closely followed by a strong commitment to academic, government, development, and policy responses. Pathways to water security capture the sequence of investments in institutions and infrastructure to reduce water-related risks and manage trade-offs. Two well-studied water management case studies illustrate the pathways to water security and the need for more systematic comparative assessment.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".