Out of oxygen: Stratification and loading drove hypoxia during a warm, wet, and productive year in a Great Lakes estuary
Bibliographic record
Abstract
Hypolimnetic hypoxia, or low oxygen in bottom waters, impairs ecosystem services of freshwater lakes and estuaries globally. Both hypoxia incidence and intensity are increasing around the world due to eutrophication and climate change. As the hypolimnion becomes hypoxic and ultimately anoxic, sediment-bound legacy phosphorus is released. Water column mixing due to large storm events or fall turnover entrains these nutrients to the surface, causing harmful algal blooms. To assess the dynamics of hypoxia throughout the growing season, we evaluated Muskegon Lake, where hypoxia recurs annually, utilizing high-frequency time-series data from the Muskegon Lake Observatory (MLO) buoy (https://www.gvsu.edu/wri/buoy/), biweekly nutrient sampling, and seasonal respiration experiments during 2021. While water-column stratification set the stage for hypolimnetic hypoxia, frequent wind-mixing events, and episodic intrusions of cold, oxygenated, upwelled Lake Michigan waters intermittently reduced the thickness or intensity of the hypoxic zone. Respiration experiments revealed that riverine and surface organic matter inputs contributed most to hypolimnetic hypoxia in the spring, whereas surface inputs did so during summer, and riverine inputs during fall, indicating seasonally variable sources drive hypoxia. Biweekly measurements indicated increased soluble reactive phosphorus in the hypolimnion during anoxia via internal phosphorus loading from the sediment with the potential for fueling surface blooms with net export of soluble reactive phosphorus and total phosphorus to nearshore Lake Michigan. Our findings on the role of seasonally changing temperature, loading, phytoplankton production, hypolimnetic respiration, and internal phosphorus loading in shaping hypoxia dynamics have relevance to similarly afflicted ecosystems in the Great Lakes Basin.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".