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Record W2313005670 · doi:10.3189/2013jog12j208

Firn profile changes in response to extreme 21st-century melting at Devon Ice Cap, Nunavut, Canada

2013· article· en· W2313005670 on OpenAlexafffundabout
Peter Bezeau, Martin Sharp, David Burgess, Gabrielle Gascon

Bibliographic record

VenueJournal of Glaciology · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsGeological Survey of CanadaNatural Resources CanadaUniversity of Alberta
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsFirnGlacierGeologyPhysical geographyArcticClimate changeAccumulation zoneAtmospheric sciencesIce coreThinningClimatologyGlacier mass balanceGeomorphologyIce streamOceanographyCryosphereSea iceGeography

Abstract

fetched live from OpenAlex

Abstract Estimating regional-scale rates of glacier mass change from repeat airborne and satellite altimetry requires assumptions about the density of the material being added to, or removed from, the glacier surface. This may vary in both space and time, especially in a period of warming climate such as has occurred in Arctic Canada since 2005. Here we compare firn properties measured on 20 shallow cores recovered from elevations of 1400–1900 m a.s.l. on Devon Ice Cap in 2012 with equivalent measurements from 34 cores recovered from the same locations between 2004 and 2011. The average density of the top 2.5 m of the firn column has increased by 13–80%. As a result, measured rates of density increase in the top 1.25 mw.e. of the firn layer have likely thinned this layer at rates of 0.021–0.168 m a −1 over the period 2004–12. These thinning rates are significant relative to local rates of surface height change measured by repeat airborne laser altimetry between 2004 and 2012 (−0.077 to −0.252 m a −1 ) and indicate that, during periods of climate warming or cooling, firn densification rate changes must be taken into account when estimating mass change rates from measured height changes.

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 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 score0.617
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.216
Teacher spread0.192 · 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

Citations49
Published2013
Admission routes3
Has abstractyes

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