 A Global Perspective on Endorheic Lake Shrinkage: Impacts of Anthropogenic and Atmospheric Factors 
Bibliographic record
Abstract
Endorheic lakes, critical components of terrestrial hydrology in closed drainage basins, serve as sensitive indicators of environmental and anthropogenic changes (Hassani et al., 2020). This study analyzed 635 endorheic lakes globally using high-resolution satellite datasets to quantify changes in surface area from 2000 to 2021 and identify the underlying causes. Of these, 134 lakes showed noticeable surface area reductions, with the highest rates observed in water-stressed regions, particularly in Asia and Semi-Arid climates. We found that anthropogenic activities, including agricultural expansion, were key drivers of shrinkage in 89 lakes, whereas meteorological factors, such as increased aridity, primarily influenced 45 lakes. For example, irrigation significantly impacted water balance in places like Wadi Al Rayan in Egypt and Chenghai Lake in China, while industrial activities like lithium mining were particularly notable in the basin of the Dongtai Jiner Lake in China. Additionally, changes in climatic variables, including reduced precipitation and heightened evapotranspiration, further exacerbated lake surface reductions in many regions. These findings highlight the complex interplay between human and natural factors affecting lake dynamics often resulting in what is referred to as anthropogenic drought. They offer valuable insights for the sustainable management of endorheic lake ecosystems, emphasizing the need for strategies that address both direct anthropogenic pressures and changes in climatic and environmental factors. Hassani, A., Azapagic, A., D'Odorico, P., Keshmiri, A., Shokri, N. (2020). Desiccation crisis of saline lakes: A new decision-support framework for building resilience to climate change. Science of the Total Environment, 703, 134718, https://doi.org/10.1016/j.scitotenv.2019.134718.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".