MétaCan
Menu
Back to cohort
Record W3009979743 · doi:10.1007/s00382-020-05191-9

Characteristics of stratospheric polar vortex fluctuations associated with sea ice variability in the Arctic winter

2020· article· en· W3009979743 on OpenAlexaboutno aff
Jinju Kim, Kwang‐Yul Kim

Bibliographic record

VenueClimate Dynamics · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsPolar vortexClimatologySea iceArctic oscillationArctic ice packGeologyArctic sea ice declineArcticArctic geoengineeringArctic dipole anomalyOceanographyCryosphereStratosphereEnvironmental scienceAtmospheric sciencesAntarctic sea iceNorthern Hemisphere

Abstract

fetched live from OpenAlex

Abstract The leading two modes of winter (November–February) Arctic sea ice cover variability and their linkage to stratospheric polar vortex variations are analyzed based on the cyclostationary EOF techniques. The first mode represents an accelerating trend of Arctic sea ice decline associated with Arctic amplification, particularly in the Barents and Kara Seas. The second mode is associated with decadal-scale phase shifts of dipole sea ice anomalies in the North Atlantic caused by NAO circulation. The first two modes of sea ice variability represent respectively a forced climate change and internal variability, and result in temporally and spatially distinct stratospheric polar vortex weakening. Sea ice reduction in the Barents and Kara Seas for the first mode is linked to a stratospheric vortex weakening during mid January–late February. The second mode with the dipole structure of positive sea ice anomalies in the Barents and Greenland Seas and negative anomalies in the Hudson Bay and Labrador Sea is related to a stratospheric vortex weakening during December–early February. The spatial evolutionary structure of anomalous polar vortex also exhibits differences between the two modes. When stratospheric anomalies are fully developed, stratospheric vortex is shifted to Eurasia in the first mode and to Europe in the second mode.

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 score0.335
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.200
Teacher spread0.190 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueClimate DynamicsSame topicArctic and Antarctic ice dynamicsFrench-language works237,207