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Record W2040924271 · doi:10.1038/ncomms7642

Glacial ice and atmospheric forcing on the Mertz Glacier Polynya over the past 250 years

2015· article· en· W2040924271 on OpenAlexaff
Philippine Campagne, Xavier Crosta, Marie-Noëlle Houssais, Didier Swingedouw, Sabine Schmidt, Antoine Martin, Emmanuel Devred, Sylvain Capo, Vincent Marieu, Ivia Closset, Guillaume Massé

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversité Laval
FundersEuropean Social FundInstitut Polaire Français Paul Emile VictorCentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsGlacierGeologyIcebergOceanographyIce calvingIce tongueSea iceIce shelfClimatologyCryosphereGeomorphology

Abstract

fetched live from OpenAlex

The Mertz Glacier Polynya off George V Land, East Antarctica, is a source of Adélie Land Bottom Water, which contributes up to ~25% of the Antarctic Bottom Water. This major polynya is closely linked to the presence of the Mertz Glacier Tongue that traps pack ice upstream. In 2010, the Mertz Glacier calved a massive iceberg, deeply impacting local sea ice conditions and dense shelf water formation. Here we provide the first detailed 250-year long reconstruction of local sea ice and bottom water conditions. Spectral analysis of the data sets reveals large and abrupt changes in sea surface and bottom water conditions with a ~70-year cyclicity, associated with the Mertz Glacier Tongue calving and regrowth dynamics. Geological data and atmospheric reanalysis, however, suggest that sea ice conditions in the polynya were also very sensitive to changes in surface winds in relation to the recent intensification of the Southern Annular 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)0.000
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 score0.247
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.257
Teacher spread0.224 · 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

Citations72
Published2015
Admission routes1
Has abstractyes

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