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Record W2604311007 · doi:10.1126/science.aai8204

Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean

2017· article· en· W2604311007 on OpenAlexaff
Igor V. Polyakov, Andrey V. Pnyushkov, Matthew B. Alkire, Igor Ashik, Till M. Baumann, Eddy C. Carmack, Ilona Goszczko, John D. Guthrie, Vladimir Ivanov, Torsten Kanzow, Richard Krishfield, R. Kwok, Arild Sundfjord, J. Morison, Robert Rember, A. V. Yulin

Bibliographic record

VenueScience · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsFisheries and Oceans Canada
FundersRussian Science FoundationNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsArctic sea ice declineHaloclineArctic ice packArctic dipole anomalyOceanographyStructural basinGeologyArcticSea iceClimatologyArctic geoengineeringAntarctic sea iceEnvironmental sciencePaleontology

Abstract

fetched live from OpenAlex

Arctic sea-ice loss is a leading indicator of climate change and can be attributed, in large part, to atmospheric forcing. Here, we show that recent ice reductions, weakening of the halocline, and shoaling of the intermediate-depth Atlantic Water layer in the eastern Eurasian Basin have increased winter ventilation in the ocean interior, making this region structurally similar to that of the western Eurasian Basin. The associated enhanced release of oceanic heat has reduced winter sea-ice formation at a rate now comparable to losses from atmospheric thermodynamic forcing, thus explaining the recent reduction in sea-ice cover in the eastern Eurasian Basin. This encroaching "atlantification" of the Eurasian Basin represents an essential step toward a new Arctic climate state, with a substantially greater role for Atlantic inflows.

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 score0.016
Threshold uncertainty score0.031

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 science0.0000.001
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 head0.013
GPT teacher head0.228
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

Citations938
Published2017
Admission routes1
Has abstractyes

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