Widespread potential for streamflow leakage across Brazil
Bibliographic record
Abstract
River-aquifer interactions play a crucial role in water availability, influencing environmental flows and impacting climate dynamics. Where groundwater tables lie below river water levels, stream water can infiltrate into the underlying aquifer, reducing streamflow. However, the prevalence of these "losing" rivers remains poorly understood due to limited national-wide in situ observations. Here we analyze water levels in 17,972 wells across Brazil to show that most of them (55%) lie below nearby stream surfaces, implying that these nearby streams are likely seeping into the subsurface. Our results demonstrate the widespread potential for stream water losses into underlying aquifers in many regions of the country, especially in areas with extensive groundwater pumping. Our direct observations underscore the importance of conjunctively managing groundwater and surface water, and highlight the widespread risk of streamflow losses to aquifers, which could impact global water access and ecosystems that rely on rivers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".