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Record W3042171967 · doi:10.1029/2020gl088550

Eddy Kinetic Energy in the Arctic Ocean From a Global Simulation With a 1‐km Arctic

2020· article· en· W3042171967 on OpenAlexaboutno aff
Qiang Wang, Nikolay Koldunov, Sergey Danilov, Dmitry Sidorenko, Claudia Wekerle, Patrick Scholz, Igor Bashmachnikov, Thomas Jung

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftEuropean Commission
KeywordsHaloclineArcticEddyGeologyClimatologyMesoscale meteorologyOceanographyOcean currentKinetic energyBoundary currentThermohaline circulationOceanic basinRange (aeronautics)Canada BasinEnvironmental scienceStructural basinMeteorologyGeomorphologyGeographyPhysicsTurbulenceSalinity

Abstract

fetched live from OpenAlex

Abstract Simulating Arctic Ocean mesoscale eddies in ocean circulation models presents a great challenge because of their small size. This study employs an unstructured‐mesh ocean‐sea ice model to conduct a decadal‐scale global simulation with a 1‐km Arctic. It provides a basinwide overview of Arctic eddy energetics. Increasing model resolution from 4 to 1 km increases Arctic eddy kinetic energy (EKE) and total kinetic energy (TKE) by about 40% and 15%, respectively. EKE is the highest along main currents over topography slopes, where strong conversion from available potential energy to EKE takes place. It is high in halocline with a maximum typically centered in the depth range of 70–110 m, and in the Atlantic Water layer of the Eurasian Basin as well. The seasonal variability of EKE along the continental slopes of southern Canada and eastern Eurasian basins is similar, stronger in fall and weaker in spring.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.259
Teacher spread0.234 · 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 designObservational
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

Citations77
Published2020
Admission routes1
Has abstractyes

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