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Record W3049252199 · doi:10.1175/jpo-d-20-0056.1

Potential Vorticity Dynamics of the Arctic Halocline

2020· article· en· W3049252199 on OpenAlexaboutno aff
Michael A. Spall

Bibliographic record

VenueJournal of Physical Oceanography · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Science Foundation of Sri LankaNational Science Foundation
KeywordsHaloclineOcean gyreGeologyPotential vorticityAnticycloneAdvectionVorticityBoundary currentClimatologyPositive vorticity advectionOceanographyWind stressBaroclinityCanada BasinOcean currentArcticMeteorologyVortexPhysicsSalinity

Abstract

fetched live from OpenAlex

Abstract An idealized two-layer shallow water model is applied to the study of the dynamics of the Arctic Ocean halocline. The model is forced by a surface stress distribution reflective of the observed wind stress pattern and ice motion and by an inflow representing the flow of Pacific Water through Bering Strait. The model reproduces the main elements of the halocline circulation: an anticyclonic Beaufort Gyre in the western basin (representing the Canada Basin), a cyclonic circulation in the eastern basin (representing the Eurasian Basin), and a Transpolar Drift between the two gyres directed from the upwind side of the basin to the downwind side of the basin. Analysis of the potential vorticity budget shows a basin-averaged balance primarily between potential vorticity input at the surface and dissipation at the lateral boundaries. However, advection is a leading-order term not only within the anticyclonic and cyclonic gyres but also between the gyres. This means that the eastern and western basins are dynamically connected through the advection of potential vorticity. Both eddy and mean fluxes play a role in connecting the regions of potential vorticity input at the surface with the opposite gyre and with the viscous boundary layers. These conclusions are based on a series of model runs in which forcing, topography, straits, and the Coriolis parameter were varied.

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 score0.211
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0000.000

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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