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Record W1985210068 · doi:10.1029/2007jf000905

Iceberg calving flux and mass balance of the Austfonna ice cap on Nordaustlandet, Svalbard

2008· article· en· W1985210068 on OpenAlexaboutno aff
Julian A. Dowdeswell, Toby Benham, Tazio Strozzi, Jon Ove Hagen

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsIcebergGeologyArctic ice packArcticIce streamSea iceAntarctic sea iceCryosphereFlux (metallurgy)ClimatologyOceanography

Abstract

fetched live from OpenAlex

Satellite radar interferometry, 60 MHz airborne ice‐penetrating radar data, and visible band satellite imagery were used to calculate the velocity structure, ice thickness, and the changing ice‐marginal extent of Austfonna (8000 km 2 and 2500 km 3 ), the largest ice cap in the Eurasian Arctic. Ice cap motion is generally less than about 10 m a −1 , except where faster flowing curvilinear features with velocities of several tens to over 200 m a −1 are present. Most drainage basins of Austfonna have undergone ice‐marginal retreat over the past few decades at an average of a few tens of meters per year. Integrating margin change around the whole ice cap gives a total area loss of about 10 km 2 a −1 . Iceberg flux from the marine margins of Austfonna is about 2.5 ± 0.5 km 3 a −1 (water equivalent), about 45% of the total calving flux from the whole Svalbard archipelago. When mass loss by iceberg production is taken into account, the total mass balance of Austfonna is negative by between about 2.5 and 4.5 km 3 a −1 . This iceberg flux represents about 33 ± 5% of total annual mass loss from Austfonna, with the remainder lost through surface ablation. Iceberg flux should be included in calculations of the total mass balance of the many large Arctic ice caps, including those located in the Russian and Canadian Arctic that end in tidewater. The neglect of this term has led to underestimates of mass loss from these ice caps and, thus, to underestimates of the contribution of Arctic ice caps to global sea level rise.

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)0.001
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 score0.015
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.289
Teacher spread0.239 · 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

Citations102
Published2008
Admission routes1
Has abstractyes

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