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Record W1910062242 · doi:10.1002/2014gl062107

Trend and interannual variability in southeast Greenland Sea Ice: Impacts on coastal Greenland climate variability

2014· article· en· W1910062242 on OpenAlexafffund
G. W. K. Moore, Fiammetta Straneo, Marilena Oltmanns

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationEuropean Centre for Medium-Range Weather ForecastsUniversität HamburgStrongNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsGeologyOceanographyFjordClimatologyFuture sea levelSea iceArctic ice packAntarctic sea iceCryosphereGlacierIcebergNorth Atlantic oscillationGreenland ice sheetIce sheetGeomorphology

Abstract

fetched live from OpenAlex

Abstract We describe the recent occurrence of a region of diminished sea ice cover or “notch” offshore of the Kangerdlugssuaq Fiord, the site of the largest tidewater glacier along Greenland's southeast coast. The notch's location is consistent with a topographically forced flux of warm water toward the fiord, and the decrease of the sea ice cover is shown to be associated with a regional warming of the upper ocean that began in the mid‐1990s. Sea ice in the vicinity of the notch also exhibits interannual variability that is shown to be associated with a seesaw in surface temperature and sea ice between southeast and northeast Greenland that is not describable solely in terms of the North Atlantic Oscillation. We therefore argue that other modes of atmospheric variability, including the Lofoten Low, are required to fully document the changes to the climate that are occurring along Greenland's east coast.

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 score0.119
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.269
Teacher spread0.254 · 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

Citations9
Published2014
Admission routes2
Has abstractyes

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