Scientists’ Warning to Humanity: Rapid degradation of the world’s large lakes
Bibliographic record
Abstract
Large lakes of the world are habitats for diverse species, including endemic taxa, and are valuable resources that provide humanity with many ecosystem services. They are also sentinels of global and local change, and recent studies in limnology and paleolimnology have demonstrated disturbing evidence of their collective degradation in terms of depletion of resources (water and food), rapid warming and loss of ice, destruction of habitats and ecosystems, loss of species, and accelerating pollution. Large lakes are particularly exposed to anthropogenic and climatic stressors. The Second Warning to Humanity provides a framework to assess the dangers now threatening the world’s large lake ecosystems and to evaluate pathways of sustainable development that are more respectful of their ongoing provision of services. Here we review current and emerging threats to the large lakes of the world, including iconic examples of lake management failures and successes, from which we identify priorities and approaches for future conservation efforts. The review underscores the extent of lake resource degradation, which is a result of cumulative perturbation through time by long-term human impacts combined with other emerging stressors. Decades of degradation of large lakes have resulted in major challenges for restoration and management and a legacy of ecological and economic costs for future generations. Large lakes will require more intense conservation efforts in a warmer, increasingly populated world to achieve sustainable, high-quality waters. This Warning to Humanity is also an opportunity to highlight the value of a long-term lake observatory network to monitor and report on environmental changes in large lake ecosystems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".