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Record W2772886405 · doi:10.1175/jpo-d-17-0190.1

Inferring Circulation and Lateral Eddy Fluxes in the Arctic Ocean’s Deep Canada Basin Using an Inverse Method

2017· article· en· W2772886405 on OpenAlexaboutno aff
Hayley Dosser, Mary‐Louise Timmermans

Bibliographic record

VenueJournal of Physical Oceanography · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsIsopycnalOcean gyreGeologyCircumpolar deep waterAdvectionOceanographyBoundary currentHydrographyPycnoclineGeostrophic windStructural basinHaloclineEddyArcticClimatologyOcean currentThermohaline circulationGeomorphologySalinityTurbulenceNorth Atlantic Deep WaterMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract The deep waters in the Canada Basin display a complex temperature and salinity structure, the evolution of which is poorly understood. The fundamental physical processes driving changes in these deep water masses are investigated using an inverse method based on tracer conservation combined with empirical orthogonal function analysis of repeat hydrographic measurements between 2003 and 2015. Changes in tracer fields in the deep Canada Basin are found to be dominated by along-isopycnal diffusion of water properties from the margins into the central basin, with advection by the large-scale Beaufort Gyre circulation as well as localized, vertical mixing playing important secondary roles. In the Barents Sea branch of the Atlantic Water layer, centered around 1200-m depth, diffusion is shown to be nearly twice as important as advection to lateral transport. Along-isopycnal diffusivity is estimated to be ~300–600 m 2 s −1 . Large-scale circulation patterns and lateral advective velocities associated with the anticyclonic Beaufort Gyre are inferred, with an average speed of 0.6 cm s −1 . Below about 1500 m, along-isopycnal diffusivity is estimated to be ~200–400 m 2 s −1 .

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 score0.185
Threshold uncertainty score0.934

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 communication0.0000.001
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 head0.018
GPT teacher head0.255
Teacher spread0.236 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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