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Record W1844857947 · doi:10.1029/2012jc008354

Atlantic water variability on the SE Greenland continental shelf and its relationship to SST and bathymetry

2012· article· en· W1844857947 on OpenAlexaff
David A. Sutherland, Fiammetta Straneo, Garry B. Stenson, Fraser Davidson, Mike O. Hammill, Aqqalu Rosing‐Asvid

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsBathymetryContinental shelfGeologyOceanographyWater massFjordWater columnIcebergGreenland ice sheetSea surface temperatureClimatologySea iceIce sheet

Abstract

fetched live from OpenAlex

Interaction of warm, Atlantic‐origin water (AW) and colder, polar origin water (PW) advecting southward in the East Greenland Current (EGC) influences the heat content of water entering Greenland's outlet glacial fjords. Here we use depth and temperature data derived from deep‐diving seals to map out water mass variability across the continental shelf and to augment existing bathymetric products. We compare depths derived from the seal dives with the IBCAO Version 3 bathymetric database over the shelf and find differences up to 300 m near several large submarine canyons. In the vertical temperature structure, we find two dominant modes: a cold mode, with the typical AW/PW layering observed in the EGC, and a warm mode, where AW is present throughout the water column. The prevalence of these modes varies seasonally and spatially across the continental shelf, implying distinct AW pathways. In addition, we find that satellite sea surface temperatures (SST) correlate significantly with temperatures in the upper 50 m ( R = 0.54), but this correlation decreases with depth ( R = 0.22 at 200 m), and becomes insignificant below 250 m. Thus, care must be taken in using SST as a proxy for heat content, as AW mainly resides in these deeper layers. Sample Unit Level Copyright

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)0.001
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 score0.005
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.044
GPT teacher head0.295
Teacher spread0.251 · 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

Citations68
Published2012
Admission routes1
Has abstractyes

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