Investigating the Thermal Stratification Patterns of a Shallow Polymictic Boreal Shield Lake and the Effectiveness of Models in Predicting Shallow Lake Thermal Profiles
Bibliographic record
Abstract
Thermal stratification is a core process of lakes which plays a critical role in shaping the ecological dynamics of lakes, influencing major processes like nutrient cycling and oxygen availability. For shallow polymictic lakes, thermal stratification patterns present some unique differences due to the complex and variable nature of these lakes. In this thesis, I examined thermal stratification in a shallow polymictic lake in northwestern Ontario during the ice-free season of 2019 to investigate the frequency, duration, and driving factors of thermal stratification. The lake underwent 146 separate stratification events, with a median duration of 2 hours per event, with a total time spent stratified of almost one third the entire study period. The primary drivers of this thermal stratification were air temperature, wind direction, and wind speed, underscoring their significance in the thermal regime of the lake. Also observed was a brief period of hypolimnetic hypoxia prior to a phytoplankton bloom, highlighting potential influence from internal nutrient loading from sediment to have occurred under low redox conditions. Another aspect of this investigation involved the evaluation of five one-dimensional models on their performance in reproducing the observed thermal stratification patterns in both the shallow polymictic lake and an adjacent shallow dimictic lake. While some models demonstrated success in simulating thermal stratification, particularly in the dimictic lake, they struggled to accurately capture the dynamic patterns observed in the polymictic lake. The most effective model calculated only 55 stratification events compared to the 146 that were observed in Lake 303, suggesting the need for highly specific modeling approaches tailored to shallow polymictic lakes. These findings exhibit the complexity of thermal stratification dynamics in shallow polymictic lakes and highlight the importance for additional field observations as well as modeling efforts to enhance our understanding of this lake type. Improving the state of modeling for these lakes is essential for accurately representing the unique nature of shallow polymictic lakes, which is crucial for effective lake management and conservation strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".