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Record W2937153686 · doi:10.1029/2019gl082110

Volume, Heat, and Freshwater Divergences in the Subpolar North Atlantic Suggest the Nordic Seas as Key to the State of the Meridional Overturning Circulation

2019· article· en· W2937153686 on OpenAlexaboutno aff
Léon Chafik, T. Rossby

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersSwedish National Space AgencyHavforskningsinstituttet
KeywordsOcean gyreOceanographyGeologyClimatologyBuoyancyShutdown of thermohaline circulationNorth Atlantic Deep WaterThermohaline circulationSubtropics

Abstract

fetched live from OpenAlex

Abstract The meridional overturning circulation (MOC) decreases rapidly in subpolar and Nordic regions where the warm upper layer loses its buoyancy due to intense heat loss, sinks, and flows south. The major volume loss of the upper limb of the MOC, ~9.6 Sv out of 18.4 ± 3.4 Sv, occurs as subduction across the Iceland Basin and Irminger Sea while the major heat loss, 273 TW out of 395 ± 74 TW is associated with the MOC branch that continues into the Nordic Seas where North Atlantic deep overflow water is produced. The 122 ± 79 TW heat flux convergence in the subpolar gyre appears to be significantly larger than various estimates of heat loss to the atmosphere. Much of the 0.09 ± 0.02 Sv freshwater divergence is presumably balanced by runoff from the Greenland shelf. These estimates suggest that the Nordic Seas, not the Labrador Sea, are key to the state of the MOC.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations136
Published2019
Admission routes1
Has abstractyes

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