Invisible water security: Moisture recycling and water resilience
Bibliographic record
Abstract
Water security is key to planetary resilience for human society to flourish in the face of global change. Atmospheric moisture recycling - the process of water evaporating from land, flowing through the atmosphere, and falling out again as precipitation over land - is the invisible mechanism by which water influences resilience, that is the capacity to persist, adapt, and transform. Through land-use change, mainly by agricultural expansion, humans are destabilizing and modifying moisture recycling and precipitation patterns across the world. Here, we provide an overview of how moisture recycling changes may threaten tropical forests, dryland ecosystems, agriculture production, river flows, and water supplies in megacities, and review the budding literature that explores possibilities to more consciously manage and govern moisture recycling. Novel concepts such as the precipitationshed allows for the source region of precipitation to be understood, addressed and incorporated in existing water resources tools and sustainability frameworks. We conclude that achieving water security and resilience requires that we understand the implications of human influence on moisture recycling, and that new research is paving the way for future possibilities to manage and mitigate potentially catastrophic effects of land use and water system change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".