Modelling the Bathymetric Influence on Ice Melt for an Idealized Ice-Covered Lake
Bibliographic record
Abstract
Despite the high abundance of lakes that experience ice cover at some point in their seasonal cycle and the important role frozen lakes play for many communities, winter limnology remains underdiscussed compared to its summer counterpart. This paper examines the effect of bathymetry on the late-winter basin-scale circulation and the associated melt rates, which remains a knowledge gap in the literature. The modelling setup considered a symmetrical truncated cone lake shape, covered uniformly by 35 cm of ice, and idealized atmospheric warming input to simulate convection-driven mixing under ice. In the absence of Coriolis forces, the simulations indicated that bathymetry does influence under-ice flow features and, under these modelled conditions, produces a signal in the ice melting pattern. As the strength of the rotational forces increased (increasing Coriolis frequency), an anticyclonic (clockwise) gyre formed and grew, resulting in a suppression of lateral heat transport from the density currents. A transition in circulation regime occurs for Rosby number ≈0.15, where the ice melt pattern reversed, revealing greater melting at the lake sides due to the heat becoming trapped there. The trend of these effects was reinforced with real bathymetry data from Base Mine Lake, a pit lake located in northeast Alberta. Despite the many differences between the idealized setup and the real lake conditions, the real ice melt compared well with the simulation without Coriolis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".