Thermal mixing patterns in Lake Ontario as revealed by novel year-round observations of thermal stratification
Bibliographic record
Abstract
Year-round records of thermal stratification in the Great Lakes are rare, and there are few observations of thermal stratification during winter. In this paper we analyze temperature data from 13 temperature logger chains and from over 130 benthic acoustic receivers that were deployed across Lake Ontario for two years. The timing and duration of the fall overturn correlates with the local average water depth, and shallow sites ( 100 m depths). Likewise, in spring the shallow sites warm faster. Lake Ontario has partial ice cover, so wind driven mixing stirs the water column throughout winter and inverse thermal stratification is largely absent. The depth-averaged winter water temperatures vary between 0 – 4oC, with the coldest temperatures (near 0.1oC) found in the shallow Kingston basin, and warmest temperatures (near 4oC) at sites near the 244 m deep Rochester Basin. Lake Ontario appears to be a warm monomictic lake, rather than having a dimictic mixing pattern – there is no sustained ice cover or inverse stratification that inhibits vertical mixing in winter. Winter is a poorly understood season for many aquatic processes, including fish bioenergetics, fish distribution, biochemical processes, invertebrate distribution and production. Moreover, lack of knowledge of winter has hampered the use of correct initial conditions for running large lake hydrodynamic models. We discuss the implications of these 2-year observations of thermal stratification in Lake Ontario for interpreting fish habitat usage and fish reproductive phenology and for fisheries management.
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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 | 0.001 | 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) | 0.001 | 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".