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Record W2811000379 · doi:10.1017/jog.2018.49

Area change of glaciers across Northern Ellesmere Island, Nunavut, between ~1999 and ~2015

2018· article· en· W2811000379 on OpenAlexafffundabout
Adrienne White, Luke Copland

Bibliographic record

VenueJournal of Glaciology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaGarfield Weston FoundationArcticNetUniversity of OttawaAssociation of Canadian Universities for Northern StudiesUniversity of Minnesota
KeywordsGlacierGeologyGlacier morphologyIce shelfIce capsSurgePhysical geographyFuture sea levelCryosphereAntarctic sea iceClimate changeIce streamClimatologySea iceOceanographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

ABSTRACT Using a variety of optical satellite scenes, this study quantifies the change in the areal extent of 1773 glaciers across Northern Ellesmere Island between ~1999 and ~2015. Our results show that the regional ice coverage decreased by 1705.3 km 2 over the ~16-year period, a loss of ~5.9%. Ice shelves had the greatest losses relative to their size, of ~42.4%. Glaciers feeding into ice shelves reduced in area by 4.7%, while tidewater glaciers reduced in area by 3.3%. Marine-terminating glaciers with floating ice tongues reduced in area by 4.9%, and 19 of these 27 ice tongues disintegrated, causing these glaciers to retreat to their grounding lines. Land-terminating glaciers lost 4.9% of their 1999 area, including the complete loss of three small ice caps (<1.5 km 2 ). Our study highlights the high sensitivity of the ice cover of Northern Ellesmere Island to recent climate warming and the continued losses that are likely to occur in the future. In particular, the ice masses most susceptible to further losses are marine-terminating glaciers with floating termini and small land-terminating ice caps at low elevations.

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 score0.023
Threshold uncertainty score0.994

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 head0.047
GPT teacher head0.280
Teacher spread0.233 · 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

Citations29
Published2018
Admission routes3
Has abstractyes

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