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Record W4409877814 · doi:10.1017/jog.2025.39

Evolving ice fraction in the shallow firn layer of Devon Ice Cap, Canada, between 2012 and 2022

2025· article· en· W4409877814 on OpenAlexafffundabout
Danielle Hallé, Wesley Van Wychen, David Burgess, Brad Danielson, Brice Noël, Peter Bezeau, Luke Copland

Bibliographic record

VenueJournal of Glaciology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of OttawaQueen's UniversityNatural Resources CanadaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaUniversity of WaterlooFonds De La Recherche Scientifique - FNRS
KeywordsFirnGeologyIce capsIce coreGeomorphologyFraction (chemistry)Physical geographyOceanographySnowGlacierGeography

Abstract

fetched live from OpenAlex

Abstract Firn can store glacial meltwater and delay contribution to sea level rise, but ice layers and ice slabs within the shallow firn layer can impede the downward percolation of melt. Here we report firn conditions along a transect on southwest Devon Ice Cap (DIC), Nunavut, and explore its response to air temperature variability over a decadal period. We present results from two field campaigns, during which six shallow firn cores were extracted along the same transect in spring 2012 and 2022. At all sites, the ice fraction (IF) was less in 2022 than in 2012, and the firn content increased. Between 2012 and 2022, the IF of the firn layer changed by −30% at the lowest elevation site (1400 m a.s.l.) and by −11% at the highest elevation site (1800 m a.s.l.) and by an average of −26% across all sites. Despite higher annual positive degree day sums during 2012–22 compared to 2002–12, cooler summers in 2013, 2018 and 2021 resulted in less ice content in the shallow firn layer. This demonstrates that the shallow firn layer can regenerate from several cooler years and highlights the nuanced response of the DIC shallow firn layer to climate warming.

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 score0.583
Threshold uncertainty score0.758

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)0.0000.000

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 head0.019
GPT teacher head0.242
Teacher spread0.223 · 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

Citations2
Published2025
Admission routes3
Has abstractyes

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