Frequency and duration of weak thermal stratification and hypoxia in the shallow western basin of Lake Erie
Bibliographic record
Abstract
Polymictic lakes are not always continuously mixed; they often experience alternating periods of mixing and weak stratification. The shallow western basin of Lake Erie is one example of a polymictic basin where the frequency of mixing has important consequences for water quality. In this study, we capture high-frequency data from the summers of 2021, 2022, and 2023, including water temperature, dissolved oxygen (DO), and water current velocity. This dataset allows us to show that hypoxic events (<2 mg/L) are triggered by episodes of weak stratification in Pigeon Bay, in western Lake Erie. Our five sampling sites in Pigeon Bay were located 50 m to 20 km from an important nearshore municipal water intake that supplies drinkable water to ∼66,800 residents and to the second largest greenhouse cluster in the world. Between June and September in these 3 years, we found that Pigeon Bay was stratified with a vertical temperature difference above 2 °C, for respectively 45 % (2021), 54 % (2022), and 25 % (2023) of the time. Significantly, all the hypoxic events were associated with stratified events. During the sampling periods, stratified events and hypoxic events were induced by either 1) local surface heating or 2) advection of hypolimnetic water from central Lake Erie. The majority of the hypoxic events in Pigeon Bay (83 % in 2021, 86 % in 2022, and 67 % in 2023) were associated with the horizontal transport of cold water that originated over 20 km from the central basin of Lake Erie.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".