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Record W2753525138 · doi:10.3189/2015jog14j147

Modern glacier velocities across the Icefield Ranges, St Elias Mountains, and variability at selected glaciers from 1959 to 2012

2015· article· en· W2753525138 on OpenAlexafffund
Alexandra Waechter, Luke Copland, Emilie Herdes

Bibliographic record

VenueJournal of Glaciology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Ottawa
FundersParks CanadaUniversity of Ottawa
KeywordsGeologySurgeGlacierIce fieldGlacier mass balanceTidewater glacier cycleClimatologyGeodesyPhysical geographyGeomorphologyIce calvingGeography

Abstract

fetched live from OpenAlex

Abstract New high-resolution velocity maps of the eastern St Elias Mountains, North America, are obtained from speckle tracking of winter 2011 and 2012 RADARSAT-2 image pairs. This includes the most complete velocity mapping to date of Hubbard Glacier, allowing for an upward revision of the Hubbard Glacier calving flux to 5.48 ± 1.16 km 3 a −1 . Combined with historical velocities from feature tracking of Landsat image pairs (1980s−2000s), and previously published results, these new velocity measurements allow for an evaluation of the interannual variability of motion at eight glaciers in this region, due to both long-term force-balance effects and surge dynamics. Multi-decadal velocities at the non-surge-type Kaskawulsh Glacier indicate little change along most of its length, except for the lowermost 10 km where deceleration has been pronounced since the late 1980s in a region that has undergone rapid recent thinning. Interannual variability of surge-type glaciers was high, with year-to-year velocity variations of up to several hundred m a −1 . These glaciers were also characterized by distinct patterns of deceleration and/or acceleration along their length.

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)0.001
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 score0.079
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.030
GPT teacher head0.252
Teacher spread0.222 · 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

Citations43
Published2015
Admission routes2
Has abstractyes

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