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Record W4386140066 · doi:10.1175/jpo-d-22-0238.1

Fresh Water and Atmospheric Cooling Control on Density-Compensated Overturning in the Labrador Sea

2023· article· en· W4386140066 on OpenAlexaboutno aff
Yana Bebieva, M. Susan Lozier

Bibliographic record

VenueJournal of Physical Oceanography · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsMeltwaterCurrent (fluid)GeologyHaloclineOceanographyWater massBoundary currentThermohaline circulationSalinityClimatologyOcean currentGlacierGeomorphology

Abstract

fetched live from OpenAlex

Abstract As they rim the basin from the southern tip of Greenland to the southern Labrador coast, the waters in the Labrador Sea boundary current undergo a significant transformation in salinity and temperature, but much less so in density. Motivated by these observations, a previously developed simple three-layer model is adapted to understand the processes responsible for this density-compensated overturning in the Labrador Sea. From our model simulations, we find that the density-compensating water mass transformation in the boundary current can be largely attributed to the combined effect of 1) direct atmospheric cooling of the relatively warm boundary current and 2) freshening due to mixing with the shallower and fresh waters derived from Greenland meltwater discharge and Arctic Ocean inflow. Freshening of the boundary current waters due to the excess of precipitation over evaporation in the basin has an important, but less impactful, role in the density compensation. Studies examining the sensitivity of the density compensation to the freshwater entry location reveal a larger impact when the freshwater enters the boundary current on the Greenland side of the basin, compared to the Labrador side. These results yield insights into how increasing meltwater in the subpolar North Atlantic will affect the overturning.

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)0.001
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 score0.024
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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