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Record W2003888558 · doi:10.3189/002214308785837048

Iceberg calving rates from northern Ellesmere Island ice caps, Canadian Arctic, 1999–2003

2008· article· en· W2003888558 on OpenAlexaffabout
Scott Williamson, Martin Sharp, Julian A. Dowdeswell, Toby Benham

Bibliographic record

VenueJournal of Glaciology · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Alberta
FundersNatural Environment Research Council
KeywordsIce calvingGlacierGeologyIcebergArcticIce streamPhysical geographyGlacier morphologyClimatologyCryosphereOceanographySea iceGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract Optical satellite imagery was used to estimate glacier surface velocities and iceberg calving rates from Agassiz and western Grant Ice Caps, Nunavut, Canada, between 1999 and 2003. The largest mean annual surface velocities ranged from ∼400 to 700 m a −1 , but velocities in the ∼100–200 m a −1 range were common. Summer velocities were up to an order of magnitude larger than annually averaged velocities. The highest velocity (∼1530 m a −1 ) was measured on the floating tongue of Lake Tuborg Glacier between 19 July and 19 August 2001. Calving rates from individual glaciers varied by up to a factor of two between successive years. Summer calving rates were ∼2–8 times larger than annual average rates. The average ratio of the calving flux due to terminus-volume change to that due to ice flow through the glacier terminus was ∼0.81 for the annual rates and ∼1.71 for summer rates. The estimated mean annual calving rate from Agassiz Ice Cap in the period 1999–2002 was 0.67 ± 0.15 km 3 a −1 , of which ∼54% emanated from Eugenie Glacier alone. This total rate is similar to a previously estimated calving rate from Devon Ice Cap.

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 score0.441
Threshold uncertainty score0.998

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)0.0030.000

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 head0.211
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

Citations42
Published2008
Admission routes2
Has abstractyes

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