Climate-driven deoxygenation of northern lakes
Bibliographic record
Abstract
Abstract Oxygen depletion constitutes a major threat to lake ecosystems and the services they provide. Most of the world’s lakes are located >45° N, where accelerated climate warming and elevated carbon loads might severely increase the risk of hypoxia, but this has not been systematically examined. Here analysis of 2.6 million water quality observations from 8,288 lakes shows that between 1960 and 2022, most northern lakes experienced rapid deoxygenation strongly linked to climate-driven prolongation of summer stratification. Oxygen levels deteriorated most in small lakes (<10 ha) owing to their greater volumetric oxygen demand and surface warming rates, while the largest lakes gained oxygen under minimal stratification changes and improved aeration at spring overturns. Seasonal oxygen consumption rates declined, despite widespread browning. Proliferating anoxia enhanced seasonal internal loading of C, P and N but depleted P long-term, indicating that deoxygenation can exhaust redox-sensitive fractions of sediment nutrient reservoirs.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".