Anoxia Begets Anoxia: a positive feedback to the deoxygenation of temperate lakes
Bibliographic record
Abstract
Declining oxygen concentrations in the deep waters of lakes worldwide pose a pressing environmental and societal challenge. Existing theory suggests that low deep-water dissolved oxygen (DO) concentrations could trigger a positive feedback through which anoxia (<1 mg/L DO) during a given summer begets increasingly frequent and severe occurrences of anoxia in following summers. Specifically, anoxic conditions can promote nutrient release from sediments, thereby stimulating phytoplankton growth and subsequent decomposition, which in turn fuels heterotrophic respiration and accelerates deep-water DO declines. However, while the individual relationships in this feedback are well established, to our knowledge there has not been a systematic analysis within or across lakes that simultaneously demonstrates all of the necessary mechanisms to produce a positive feedback that reinforces anoxia. Here, we compiled data from 656 widespread temperate lakes and reservoirs to analyze the proposed Anoxia Begets Anoxia (ABA) feedback. Lakes in the dataset span a broad range of surface area (1–126,909 ha), maximum depth (6–370 m), and morphometry, with a median time series length of 30 years at each lake. Using linear mixed models, we found support for each of the feedback relationships between lower DO concentrations, higher phosphorus concentrations, higher chlorophyll-a concentrations, and greater volume-weighted deep-water oxygen demand. Likewise, we found further support for these relationships within time series data at individual lakes. Our results indicate that the strength of these feedback relationships may vary with lake-specific characteristics: for example, we found that surface phosphorus concentrations were more positively associated with chlorophyll-a in high-phosphorus lakes, and oxygen demand had a stronger influence on late-summer DO concentrations in large (high-surface-area) lakes. Taken together, these results support the existence of a positive feedback that could magnify the effects of climate change and other anthropogenic pressures driving the development of anoxia in lakes around the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".