MétaCan
Menu
Back to cohort
Record W2305109427 · doi:10.1088/1748-9326/11/3/034019

Importance of combined winter and summer Arctic Oscillation (AO) on September sea ice extent

2016· article· en· W2305109427 on OpenAlexafffund
Masayo Ogi, Søren Rysgaard, David G. Barber

Bibliographic record

VenueEnvironmental Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Manitoba
FundersCanada Excellence Research Chairs, Government of CanadaDivision of Arctic SciencesCanada Research ChairsArcticNet
KeywordsClimatologyAnticycloneArctic oscillationArctic ice packArcticAtmospheric circulationEnvironmental scienceAntarctic oscillationSea iceNorth Atlantic oscillationArctic dipole anomalyArctic sea ice declineOceanographyNorthern HemisphereGeologyEl Niño Southern OscillationAntarctic sea ice

Abstract

fetched live from OpenAlex

We examine the influence of winter and summer Arctic Oscillation (AO) on variations in the September Arctic sea ice extent (SIE). The winter and summer atmospheric patterns associated with year-to-year variations and detrended September SIE correlate with the positive winter AO and the negative summer AO, respectively. However, the interannual variations of winter and summer AO indices after 2007 are more weakly connected with year-to-year variations in the September SIE. Since 2007, the surface air temperatures over the Beaufort, Chukchi and East Siberian Seas are related to the interannual variations of the September SIE. Recent summer atmospheric patterns associated with the September SIE correlate with the summer AO pattern, but the summer anticyclonic circulation over the Arctic favours the recent low September SIE more than the seesaw pattern between mid- and high- latitudes. Recent winters' positive AO have not contributed to the recent low September SIE because winter anticyclonic circulation over northern Eurasia is more directly connected with recent September SIE.

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 categoriesInsufficient payload (model declined to judge)
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 score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.251
Teacher spread0.229 · 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.

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

Citations51
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Research LettersSame topicArctic and Antarctic ice dynamicsFrench-language works237,207