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Record W4400359726 · doi:10.5194/tc-18-3159-2024

Two-dimensional numerical simulations of mixing under ice keels

2024· article· en· W4400359726 on OpenAlexafffund
Sam De Abreu, Rosalie M. Cormier, Mikhail G. Schee, Varvara Zemskova, Erica Rosenblum, Nicolas Grisouard

Bibliographic record

Venue˜The œcryosphere · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of ManitobaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKeelSea iceGeologyStratification (seeds)ClimatologyAtmospheric sciencesOceanography

Abstract

fetched live from OpenAlex

Abstract. Changes in sea ice conditions directly impact the way the wind transfers energy to the Arctic Ocean. The thinning and increasing mobility of sea ice is expected to change the size and speed of ridges on the underside of ice floes, called ice keels, which cause turbulence and impact upper-ocean stratification. However, the effects of changing ice keel characteristics on below-ice mixing are difficult to determine from sparse observations and have not been directly investigated in numerical or laboratory experiments. Here, for the first time, we examine how the size and speed of an ice keel affect the mixing of various upper-ocean stratifications using 16 two-dimensional numerical simulations of a keel moving through a two-layer flow. We find that the irreversible ocean mixing and the characteristic depth over which mixing occurs each vary significantly across a realistic parameter space of keel sizes, keel speeds, and ocean stratifications. Furthermore, we find that mixing does not increase monotonically with ice keel depth and speed but instead depends on the emergence and propagation of vortices and turbulence. These results suggest that changes to ice keel speed and depth may have a significant impact on below-ice mixing across the Arctic Ocean and highlight the need for more realistic numerical simulations and observational estimates of ice keel characteristics.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.029
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.237
Teacher spread0.224 · 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.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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