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Record W1715612442 · doi:10.1002/jgrc.20174

Seasonal variability of water masses and transport on the Antarctic continental shelf and slope in the southeastern Weddell Sea

2013· article· en· W1715612442 on OpenAlexaff
Jennifer Graham, Karen J. Heywood, Cédric Chavanne, Paul R. Holland

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversité du Québec à Rimouski
FundersNatural Environment Research CouncilSight Research UK
KeywordsPycnoclineOceanographyGeologyAdvectionContinental shelfEkman transportSalinityWater massMooringSea iceUpwellingClimatology

Abstract

fetched live from OpenAlex

An array of five moorings was deployed from February 2009 to February 2010 across the Antarctic shelf and slope in the southeastern Weddell Sea (~18°W). Observations demonstrate the key processes responsible for variability in water masses and transport in the region. Rapid fluctuations in temperature and salinity throughout the year are linked with variability in wind stress over the array. This causes the deepening or shoaling of the pycnocline, past the depth of the moorings. In the upper 500 m, the seasonal cycle in salinity shows freshening in autumn, with the strongest freshening at the shallowest mooring (~250 m), furthest on‐shelf. The sea ice concentration over the array exceeds 90% during this period and contributes a positive salt flux into the ocean during autumn. Freshening begins during strong along‐shore (easterly) winds in late April 2009. This demonstrates that variations in Ekman transport and wind‐driven mixing play a key role in determining the salinity of shelf waters around Antarctica. Transport of the Antarctic Slope Current also shows a seasonal cycle with a maximum during late April. Model simulations show the importance of along‐shore advection, as the arrival of a fresh anomaly from upstream determines the timing of the salinity minimum at the array. These processes are likely to be important for other regions around the Antarctic continent.

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)0.003
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 score0.009
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.250
Teacher spread0.230 · 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

Citations38
Published2013
Admission routes1
Has abstractyes

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