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Record W2331762747 · doi:10.3189/002214311798843287

Glacier change on Axel Heiberg Island, Nunavut, Canada

2011· article· en· W2331762747 on OpenAlexafffundabout
Laura Thomson, G. R. Osinski, C. Simon L. Ommanney

Bibliographic record

VenueJournal of Glaciology · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsWestern University
FundersCanadian Space AgencyAssociation of Canadian Universities for Northern StudiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsGlacierGeologyArcticGlacier morphologyPhysical geographyPeriod (music)Tidewater glacier cycleIce capsOceanographyArctic ice packGeomorphologyAntarctic sea iceGeographyIce calving

Abstract

fetched live from OpenAlex

Abstract Historical records are valuable for assessing glacier change in the Canadian High Arctic. Ommanney’s (1969) detailed inventory of Axel Heiberg Island glaciers, based on photography from 1958–59, has been revisited, converted into digital format and compared to glacier extents mapped from 1999–2000 satellite imagery. Our results show that the island-wide ice coverage decreased by 15.92 km 2 in the 42 year period, a loss of <1%. However, two trends are apparent: one of advance or minor retreat from basins hosting outlet glaciers from Müller and Steacie Ice Caps, and one of significant retreat, on the order of 50–80%, for independent ice masses, which include valley glaciers, mountain glaciers, glacierets, and ice caps smaller than 25 km 2 . If the contributions to ice advance of only three surging glaciers are removed, then the island-wide ice loss approaches 60 km 2 . Furthermore, it is notable that 90% of ice masses smaller than 0.2 km 2 disappeared entirely during the 42 year study period, an observation confirmed by field studies. Successful predictions from the original inventory are highlighted and the likely mechanisms driving the observed advances and retreats are discussed.

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 score0.691
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 head0.057
GPT teacher head0.217
Teacher spread0.160 · 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

Citations25
Published2011
Admission routes3
Has abstractyes

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