Global Lake Health in the Anthropocene: Societal Implications and Treatment Strategies
Bibliographic record
Abstract
Abstract The world's 1.4 million lakes (≥10 ha) provide many ecosystem services that are essential for human well‐being; however, only if their health status is good. Here, we reviewed common lake health issues and classified them using a simple human health‐based approach to outline that lakes are living systems that are in need of oxygen, clean water and a balanced energy and nutrient supply. The main reason for adopting some of the human health terminology for the lake health classification is to increase the awareness and understanding of global lake health issues. We show that lakes are exposed to various anthropogenic stressors which can result in many lake health issues, ranging from thermal, circulatory, respiratory, nutritional and metabolic issues to infections and poisoning. Of particular concern for human well‐being is the widespread lake drying, which is a severe circulatory issue with many cascading effects on lake health. We estimated that ∼115,000 lakes evaporate twice as much water as they gain from direct precipitation, making them vulnerable to potential drying if inflowing waters follow the drying trend, putting more than 153 million people at risk who live in close vicinity to those lakes. Where lake health issues remain untreated, essential ecosystem services will decline or even vanish, posing a threat to the well‐being of millions of people. We recommend coordinated multisectoral and multidisciplinary prevention and treatment strategies, which need to include a follow‐up of the progress and an assessment of the resilience of lakes to intensifying threats. Priority should be given to implementing sewage water treatment, mitigating climate change, counteracting introductions of non‐native species to lakes and decreasing uncontrolled anthropogenic releases of chemicals into the hydro‐, bio‐, and atmosphere.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".