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Record W4410302695 · doi:10.1080/07055900.2025.2497241

Modelling the Bathymetric Influence on Ice Melt for an Idealized Ice-Covered Lake

2025· article· en· W4410302695 on OpenAlexafffundvenueabout
Dana Arends, Edmund W. Tedford, Jason Olsthoorn

Bibliographic record

VenueATMOSPHERE-OCEAN · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of British ColumbiaQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBathymetryGeologyMelt pondSea iceOceanographyArctic ice packAntarctic sea ice

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.235
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes4
Has abstractyes

Explore more

Same venueATMOSPHERE-OCEANSame topicArctic and Antarctic ice dynamicsFrench-language works237,207