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Record W4386316823 · doi:10.1126/science.adh5158

Fluctuating Atlantic inflows modulate Arctic atlantification

2023· article· en· W4386316823 on OpenAlexaboutno aff
Igor V. Polyakov, Randi B. Ingvaldsen, Andrey V. Pnyushkov, Uma S. Bhatt, Jennifer A. Francis, Markus Janout, R. Kwok, Øystein Skagseth

Bibliographic record

VenueScience · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsOceanographyArctic ice packSea iceArcticArctic dipole anomalyArctic sea ice declineClimatologyArctic geoengineeringStratification (seeds)GeologyArctic ecologySubarctic climateClimate changeAnticycloneCanada BasinThe arcticDrift iceEnvironmental science

Abstract

fetched live from OpenAlex

Enhanced warm, salty subarctic inflows drive high-latitude atlantification, which weakens oceanic stratification, amplifies heat fluxes, and reduces sea ice. In this work, we show that the atmospheric Arctic Dipole (AD) associated with anticyclonic winds over North America and cyclonic winds over Eurasia modulates inflows from the North Atlantic across the Nordic Seas. The alternating AD phases create a "switchgear mechanism." From 2007 to 2021, this switchgear mechanism weakened northward inflows and enhanced sea-ice export across Fram Strait and increased inflows throughout the Barents Sea. By favoring stronger Arctic Ocean circulation, transferring freshwater into the Amerasian Basin, boosting stratification, and lowering oceanic heat fluxes there after 2007, AD+ contributed to slowing sea-ice loss. A transition to an AD- phase may accelerate the Arctic sea-ice decline, which would further change the Arctic climate system.

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 score0.010
Threshold uncertainty score0.021

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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

Citations147
Published2023
Admission routes1
Has abstractyes

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