Ice and winter dissolved oxygen modelling in Lake Winnipeg
Bibliographic record
Abstract
For lakes experiencing extended ice-cover seasons, ice phenology has a substantive impact on thermal structure and dissolved oxygen (DO) dynamics during winters. This study applied a three-dimensional lake model (AEM3D) in Lake Winnipeg over 2016-2018, spanning two full winter seasons. Sensitivity analysis showed that the modeled ice cover thickness, formation, and duration were most sensitive to snow depth and snow/ice albedo. The model well simulated the ice freeze-up timing with less than 5 days discrepancy, but it underestimated the ice cover thickness and overestimated the ice cover duration in the unusual warm winter (2016-2017). Inverse stratification was developed under the ice, but the model could not fully reproduce it due to a lack of a sediment heat flux component. DO decreased with the formation of ice cover, leading to bottom hypoxia over the lake. The model indicates that ice phenology (i.e., ice cover duration, and blue/white ice thickness) affects the extent of winter hypoxia. We observed the DO decreased to < 2 mg L-1 (i.e., anoxia) in the North Basin, along with inverse stratification forming near lakebed, and an oxygen depletion rate reached 0.14 mg L-1 d-1 in the winter of 2016-17. The model captured but underestimated DO decline near the lakebed and simulated around 8% and 70% of lake area reached anoxia in winters of 2016-17 and 2017-18, respectively. This work provides insight into ice formation, under-ice thermal structure, and winter oxygen concentrations in a large prairie lake, and the role of changing ice phenology on northern lake ecology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".