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Record W4205483379 · doi:10.1038/s41467-021-27641-6

Sea-ice retreat suggests re-organization of water mass transformation in the Nordic and Barents Seas

2022· article· en· W4205483379 on OpenAlexafffund
G. W. K. Moore, Kjetil Våge, Ian A. Renfrew, Robert S. Pickart

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsDairy Farmers of OntarioUniversity of Toronto
FundersNatural Environment Research CouncilEuropean Centre for Medium-Range Weather ForecastsSight Research UKNatural Sciences and Engineering Research Council of CanadaTrond Mohn stiftelseNational Science Foundation
KeywordsWater massEnvironmental scienceClimatologySea iceClimate changeOceanographyCurrent (fluid)Zonal and meridionalGeologyAtmospheric sciences

Abstract

fetched live from OpenAlex

Water mass transformation in the Nordic and Barents Seas, triggered by air-sea heat fluxes, is an integral component of the Atlantic Meridional Overturning Circulation (AMOC). These regions are undergoing rapid warming, associated with a retreat in ice cover. Here we present an analysis covering 1950-2020 of the spatiotemporal variability of the air-sea heat fluxes along the region's boundary currents, where water mass transformation impacts are large. We find there is an increase in the air-sea heat fluxes along these currents that is a function of the currents' orientation relative to the axis of sea-ice change suggesting enhanced water mass transformation is occurring. Previous work has shown a reduction in heat fluxes in the interior of the Nordic Seas. As a result, a reorganization seems to be underway in where water mass transformation occurs, that needs to be considered when ascertaining how the AMOC will respond to a warming climate.

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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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

Citations50
Published2022
Admission routes2
Has abstractyes

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