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Record W2057169085 · doi:10.1029/2006gl026286

Intra‐seasonal relationship between the Northern Hemisphere sea ice variability and the North Atlantic Oscillation

2006· article· en· W2057169085 on OpenAlexaboutno aff
Kentaro Yamamoto, Yoshihiro Tachibana, Meiji Honda, Jinro Ukita

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsNorthern HemisphereClimatologyRossby waveNorth Atlantic oscillationGeologySeesaw molecular geometrySea iceOceanographyPacific decadal oscillationSea surface temperature

Abstract

fetched live from OpenAlex

An intra‐seasonal relationship, including a possible feedback, is investigated between the North Atlantic Oscillation (NAO) and a hemispheric‐scale seesaw‐like pattern dominant in sea ice variability over the wintertime Northern Hemisphere, with one polarity in the Bering and Labrador Seas and the other in the Okhotsk and Greenland‐Barents Seas. Statistical analysis using observational data suggests that a particular phase of NAO and anomalous atmospheric circulation in the Pacific during early winter contribute toward the development of the midwinter hemispheric sea‐ice seesaw. In contrast, the ice seesaw tends to damp the preexisting NAO signal during late winter, indicating a reversal of the phase relation between them. This NAO damping may be triggered by the Pacific sea‐ice anomalies. Results from numerical experiments generally support this notion and further suggests a stationary Rossby wave train emanated from the North Pacific as a dynamical mechanism for damping the NAO signal.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.032
GPT teacher head0.274
Teacher spread0.243 · 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

Citations43
Published2006
Admission routes1
Has abstractyes

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