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Record W2980737410 · doi:10.1029/2019gl083722

Spatiotemporal Variability of Sea Ice in the Arctic's Last Ice Area

2019· article· en· W2980737410 on OpenAlexaffabout
G. W. K. Moore, Axel Schweiger, Jinlun Zhang, Michael Steele

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
FundersOffice of Naval ResearchNational Aeronautics and Space AdministrationNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsSea iceArctic ice packDrift iceAntarctic sea iceGeologyOceanographyCryosphereArchipelagoArctic sea ice declineArcticClimatologyArctic geoengineeringPhysical geographyGeography

Abstract

fetched live from OpenAlex

Abstract The Arctic Ocean's oldest and thickest sea ice lies along the ~2,000 km arc from the western Canadian Arctic Archipelago to the northern coast of Greenland. Climate models suggest that this region will be the last to lose its perennial ice cover, thus providing an important refuge for ice‐dependent species. However, remarkably little is known about the climate or characteristics of the sea ice in this remote and inhospitable region. Here, we use the Pan‐Arctic Ice Ocean Modeling and Assimilation System model to show that the ice cover in the region is very dynamic, with changes occurring at a rate twice that of the Arctic Ocean as a whole. However, there are some differences in the changing nature of the ice cover between the eastern and western regions of the Last Ice Area, which include different timing of the annual minimum in ice thickness as well as distinct ice motion patterns associated with ice thickness extrema.

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)0.002
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 score0.019
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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

Citations87
Published2019
Admission routes2
Has abstractyes

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