Oxygen dynamics in the bottom waters of lakes: Understanding the past to predict the future
Bibliographic record
Abstract
Dissolved oxygen concentrations are declining in the bottom waters of many lakes around the world, posing critical water quality concerns. Throughout my dissertation, I assessed how bottom-water dissolved oxygen may mediate the effects of climate and land use change on water quality in lakes. First, I characterized causes of variation in summer bottom-water temperature and dissolved oxygen. I demonstrated that spring air temperatures may play a greater role than summer air temperatures in shaping summer bottom-water dynamics. I then characterized the effects of declining bottom-water oxygen concentrations across diverse scales of analysis (i.e., using microcosm incubations, whole-ecosystem oxygenation experiments, and data analysis of >600 widespread lakes). I found that low dissolved oxygen concentrations contributed to release of nutrients and organic carbon from lake sediments, potentially altering the role of lakes in global biogeochemical cycles. Importantly, I also found support for a previously-hypothesized Anoxia Begets Anoxia feedback, whereby bottom-water anoxia (i.e., no dissolved oxygen) in a given year promotes increasingly severe occurrences of anoxia in following summers. This finding demonstrates the need for forecasts of future oxygen dynamics in lakes, as management actions to preempt the first occurrence of anoxia will be more effective than actions to restore ecological function after oxygen concentrations have already declined. To build the capacity for such forecasts, I led a systematic review of ecological forecasting literature that characterized the state of the field, emerging best practices, and relative predictability of four ecological variables. Combined, my dissertation provides a mechanistic examination of the effects of climate change on water quality in lakes worldwide, ultimately helping to anticipate, mitigate, and preempt future water quality declines.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".