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Record W1989153935 · doi:10.1175/jpo-d-11-01.1

Impact of Shelf–Basin Freshwater Transport on Deep Convection in the Western Labrador Sea

2011· article· en· W1989153935 on OpenAlexaboutno aff
Timothy P. McGeehan, Wieslaw Maslowski

Bibliographic record

VenueJournal of Physical Oceanography · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsOceanographyGeologySea iceClimatologyOcean currentArcticEddyThermohaline circulationConvectionArctic ice packMeteorologyGeographyTurbulence

Abstract

fetched live from OpenAlex

Abstract Freshwater exiting the Arctic Ocean through the Canadian Arctic Archipelago (CAA) has been shown to affect meridional overturning circulation and thereby the global climate system. However, because of constraints of spatial resolution in most global ocean models, neither the flow of low salinity water through the CAA to the Labrador Sea nor the eddy activity that may transport freshwater from the shelf to areas of open ocean convection can be directly simulated. To address these issues, this study uses a high-resolution ice–ocean model of the pan-Arctic region with a realistic CAA and forced with realistic atmospheric data. This model resolves conditions in the Arctic Ocean upstream of the Labrador Sea and is coupled to a thermodynamic–dynamic sea ice model that responds to the atmospheric forcing. The major shelf–basin exchange of liquid freshwater occurs south of Hamilton Bank, whereas the largest ice flux occurs in the northwest of the basin. Freshwater flux anomalies entering the Labrador Sea through Davis Strait do not immediately affect deep convection. Instead, eddies acting on shorter time scales can move freshwater to locations of active convection and halt the process. Convection is modulated by the position of the ice edge, highlighting the critical need for a coupled ice–ocean model. Finally, the size of eddies and the short duration of events demonstrate the need for high resolution, both spatial and temporal.

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 score0.004
Threshold uncertainty score0.251

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 communication0.0000.000
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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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

Citations31
Published2011
Admission routes1
Has abstractyes

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