Episodic flooding causes sudden deoxygenation shocks in human-dominated rivers
Bibliographic record
Abstract
Dissolved oxygen (DO) sustains river ecosystems, but the effects of hydrological extremes remain poorly understood. While high river discharge (Q) enhances aeration, floods also deliver oxygen-consuming pollutants, making net impacts uncertain. Here, we analyze daily DO and its percent saturation (DO%sat), and Q in 1156 Chinese rivers over three years. We show that DO and DO%sat decrease with rising Q in 69.1% and 55.7% of rivers, respectively. Floods (Q > 95th percentile) cause abrupt declines in both DO (19.7%) and DO%sat (16.2%) in 80.1% and 69.4% of the rivers, respectively, with the sharpest declines in agricultural and urban areas. These abrupt deoxygenation events link to increased ammonium and land-use intensity, causing more frequent hypoxia in developed regions. Contrary to initial expectations, floods often reduce oxygen levels, with faster recovery in urbanized regions. As climate change intensifies flooding, such sudden deoxygenation shocks may degrade aquatic ecosystems particularly in human-altered landscapes. “Dissolved oxygen (DO) sustains river ecosystems, but the effects of hydrological extremes remain poorly understood. Here it is shown that sudden floods cause abrupt declines in DO, suggesting that increased future flooding may lead to the degradation of aquatic ecosystems.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".