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Record W1858207246 · doi:10.1002/2013gl058558

Glacier velocities and dynamic ice discharge from the Queen Elizabeth Islands, Nunavut, Canada

2013· article· en· W1858207246 on OpenAlexafffundabout
Wesley Van Wychen, David Burgess, Laurence Gray, Luke Copland, Martin Sharp, Julian A. Dowdeswell, Toby Benham

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of AlbertaUniversity of OttawaGeological Survey of CanadaNatural Resources Canada
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaNatural Resources CanadaCanadian Space AgencySight Research UKArcticNetUniversity of Ottawa
KeywordsGlacierSurgeArcticArchipelagoClimatologyGeologyIce streamGlacier ice accumulationGroenlandiaArctic ice packPhysical geographyOceanographyCryosphereAntarctic sea iceSea iceIce sheetGeographyGeomorphology

Abstract

fetched live from OpenAlex

Abstract Recent studies indicate an increase in glacier mass loss from the Canadian Arctic Archipelago as a result of warmer summer air temperatures. However, no complete assessment of dynamic ice discharge from this region exists. We present the first complete surface velocity mapping of all ice masses in the Queen Elizabeth Islands and show that these ice masses discharged ~2.6 ± 0.8 Gt a −1 of ice to the oceans in winter 2012. Approximately 50% of the dynamic discharge was channeled through non surge‐type Trinity and Wykeham Glaciers alone. Dynamic discharge of the surge‐type Mittie Glacier varied from 0.90 ± 0.09 Gt a −1 during its 2003 surge to 0.02 ± 0.02 Gt a −1 during quiescence in 2012, highlighting the importance of surge‐type glaciers for interannual variability in regional mass loss. Queen Elizabeth Islands glaciers currently account for ~7.5% of reported dynamic discharge from Arctic ice masses outside Greenland.

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 score0.386
Threshold uncertainty score0.869

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 science0.0000.000
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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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

Citations67
Published2013
Admission routes3
Has abstractyes

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