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Record W2623570314 · doi:10.1002/2017jc012921

Tracking Labrador Sea Water property signals along the Deep Western Boundary Current

2017· article· en· W2623570314 on OpenAlexafffundabout
Isabela Le Bras, Igor Yashayaev, John M. Toole

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersDivision of Ocean SciencesFisheries and Oceans CanadaNational Science Foundation
KeywordsBoundary currentGeologyAdvectionOceanographyWater massCurrent meterCurrent (fluid)Anomaly (physics)Ocean currentNorth Atlantic oscillationClimatology

Abstract

fetched live from OpenAlex

Abstract Observations of the Deep Western Boundary Current (DWBC) at Line W on the western North Atlantic continental slope southeast of Cape Cod from 1995 to 2014 reveal water mass changes that are consistent with changes in source water properties upstream in the Labrador Sea. This is most evident in the cold, dense, and deep class of Labrador Sea Water (dLSW) that was created and progressively replenished and deepened by recurring winter convection during the severe winters of 1987–1994. The arrival of this record cold, fresh, and low potential vorticity anomaly at Line W lags its formation in the Labrador Sea by 3–7 years. Complementary observations along the path of the DWBC provide further evidence that this anomaly is advected along the boundary and indicate that stirring between the boundary and the interior intensifies south of the Flemish Cap. Finally, the consistency of the data with realistic advective and mixing time scales is assessed using the Waugh and Hall (2005) model framework. The data are found to be best represented by a mean transit time of 5 years from the Labrador Sea to Line W, with a leading order role for both advection by the DWBC and mixing between the boundary flow and interior waters.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.058
GPT teacher head0.325
Teacher spread0.268 · 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.

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

Citations55
Published2017
Admission routes3
Has abstractyes

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