Long-term dynamics of dissolved oxygen and isotopic composition in Lake Erie and Lake Ontario: Implications for eutrophication and ecosystem health
Bibliographic record
Abstract
This study, focusing on Great Lakes Erie and Ontario, Canada, explores dissolved oxygen (DO) dynamics and its stable isotopic composition ( δ 18 O DO ) in the Great Lakes. Using historical dissolved oxygen data from 1965 to 2021, combined with synoptic isotopic data collected in the early 2000s, the research examined the spatiotemporal trends in DO and δ 18 O-DO to assess the effects of biotic and abiotic processes such as photosynthesis, respiration, and atmospheric gas exchange. The study revealed significant seasonal and depth-related variations in DO levels, with hypolimnetic hypoxia and metalimnetic oxygen minima (MOM) observed in both lakes. With its comparatively shallow depth and higher nutrient loads, Lake Erie exhibited more pronounced dissolved oxygen fluctuations, with DO levels reaching as low as 3.65 mg L −1 and δ 18 O DO values falling below +24.6 ‰. These findings indicated Lake Erie’s more dynamic metabolic environment, particularly during the thermally stratified season. In contrast, Lake Ontario showed more stable oxygen concentration levels, with occasional dissolved oxygen depletions down to 6.80 mg L −1 and δ 18 O DO values up to +32.1 ‰, suggesting more localised influences of external inputs and biological processes. This study provides a long-term perspective on dissolved oxygen status in these lakes and offers essential insights into their ecological health. This work contributes valuable data for managing and protecting the Great Lakes ecosystem.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".