Considerable gaps in our global knowledge of potential groundwater accessibility
Why this work is in the frame
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Bibliographic record
Abstract
At what depth groundwater can be found below the land surface is key to understanding whether it ispotentially accessible to ecosystems and humans, or what role it plays in the water cycle. Knowledge ofground-water table depth (WTD) exists at regional scales in many places, but a bottom-up knowledgeaggregation to obtain a coherent global picture is exceptionally challenging. Uncertainty in global-scaleWTD knowledge severely affects our ability to assess groundwater’s future role in a water cycle altered bychanges in climate, land use, and human water use. Global groundwater models offer a top-down pathwayto gain this knowledge. However, we find them highly uncertain: four models investigated show WTDdisagreements of more than 100 m for one-third of the global land area. Averaged across the models, weestimate that 23% [most deviating model: 71%] of the land area contains shallow groundwater potentiallyaccessible to ecosystems and humans, <10m depth, 57% [29%] is potentially accessible to humans throughpumping, 10-100m, while 20% [0.01%] is potentially too costly to access or inaccessible, >100m.Depending on the model, +-63% of global forest coverage and +-54% of irrigated land is inside areas ofpotentially ecosystem-accessible water, and +-33% of the global population lives in areas with potentiallyhuman-accessible groundwater. These results add significant uncertainties to any global-scale analysis,which will not significantly reduce without dedicated efforts. We outline three pathways to reduce thisuncertainty through better global datasets, alternative strategies for model evaluation, and greatercooperation with experts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it