Water security in one blue planet: twenty-first century policy challenges for science
Bibliographic record
Abstract
Water-related risks threaten society at the local, national and global scales in our inter-connected and rapidly changing world. Most of the world's poor are deeply water insecure and face intolerable water-related risks associated with complex hydrology. Most of the world's wealthy face lower water-related risks and less complex hydrology. This inverse relationship between hydrological complexity and wealth contributes to a divided world. This must be addressed if global water security is to be achieved. Using a risk-based framework provides the potential to link the current policy-oriented discourse on water security to a new and rigorous science-based approach to the description, measurement, analysis and management of water security. To provide the basis for this science-based approach, we propose an encompassing definition rooted in risk science: water security is a tolerable level of water-related risk to society. Water security policy questions need to be framed so that science can marshal interdisciplinary data and evidence to identify solutions. We join a growing group of scientists in asserting a bold vision for science leadership, calling for a new and comprehensive understanding of the planet's water system and society's water needs.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".