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Record W2582073870 · doi:10.1002/2016jc012235

Water mass modification and mixing rates in a 1/12° simulation of the Canadian Arctic Archipelago

2017· article· en· W2582073870 on OpenAlexaffabout
Kenneth Hughes, Jody Klymak, Xianmin Hu, Paul G. Myers

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of AlbertaUniversity of Victoria
Fundersnot available
KeywordsArchipelagoBuoyancySillAdvectionMixing (physics)Water massFlux (metallurgy)IsopycnalMass fluxOceanographyGeologyArcticAtmospheric sciencesEnvironmental scienceClimatologyMechanicsChemistryPhysics

Abstract

fetched live from OpenAlex

Strong spatial differences in diapycnal mixing across the Canadian Arctic Archipelago are diagnosed in a 1/12° basin-scale model. Changes in mass flux between water flowing into or out of several regions are analyzed using a volume-integrated advection–diffusion equation, and focus is given to denser water, the direct advective flux of which is mediated by sills. The unknown in the mass budget, mixing strength, is a quantity seldom explored in other studies of the Archipelago, which typically focus on fluxes. Regionally averaged diapycnal diffusivities and buoyancy fluxes are up to an order of magnitude larger in the eastern half of the Archipelago relative to those in the west. Much of the elevated mixing is concentrated near sills in Queens Channel and Barrow Strait, with stronger mixing particularly evident in the net shifts of the densest water to lower densities as it traverses these constrictions. Associated with these shifts are areally averaged buoyancy fluxes up to m2 s−3 through the 1027 kg m−3 isopycnal surface, which lies at approximately 100 m depth. This value is similar in strength to the destabilizing buoyancy flux at the ocean surface during winter. Effective diffusivities estimated from the buoyancy fluxes can exceed m2 s−1, but are often closer to m2 s−1 across the Archipelago. Tidal forcing, known to modulate mixing in the Archipelago, is not included in the model. Nevertheless, mixing metrics derived from our simulation are of the same order of magnitude as the few comparable observations.

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)0.001
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 score0.139
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.050
GPT teacher head0.317
Teacher spread0.266 · 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

Citations28
Published2017
Admission routes2
Has abstractyes

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