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Record W2093707014 · doi:10.3189/002214311797409767

Sublimation and surface energy budget of Taylor Glacier, Antarctica

2011· article· en· W2093707014 on OpenAlexaff
Andrew Bliss, Kurt M. Cuffey, Jeffrey L. Kavanaugh

Bibliographic record

VenueJournal of Glaciology · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Alberta
FundersNational Science Foundation
KeywordsSublimation (psychology)GeologyGlacierKatabatic windAblation zoneAccumulation zoneGlacier ice accumulationLapse rateAtmospheric sciencesClimatologyIce streamGeomorphologyCryosphereSea ice

Abstract

fetched live from OpenAlex

Abstract Taylor Glacier, an outlet of the East Antarctic ice sheet, flows through the Transantarctic Mountains and terminates in the Dry Valleys. Understanding how this glacier fluctuates is important for studies of glacial geology, paleoclimate, ice dynamics and ecology. Sublimation is the primary mass-loss process for most of the glacier. Four years of specific balance measurements from the ablation zone show sublimation rates up to 40 cm a −1 . We used data from an array of weather stations as inputs to a model for latent heat flux and hence sublimation rate. Calculated and measured ablation rates agree to within uncertainties, indicating that wind speed and vapour pressure gradient (a function of temperature and humidity) are the governing variables, as expected from theory. Measurements and model results together allowed us to examine the spatial and temporal variations of sublimation on the glacier. On average, sublimation is about two times faster in summer than winter. Rapid sublimation occurs during storms and katabatic wind events, but such periods contribute less to the annual total than do slow, persistent losses. Spatially, sublimation reaches a maximum midway along the glacier, where descending surface air currents are focused by the topography of the aptly named tributary, Windy Gully.

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 score0.007
Threshold uncertainty score0.794

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)0.0010.000

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 head0.029
GPT teacher head0.212
Teacher spread0.183 · 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

Citations58
Published2011
Admission routes1
Has abstractyes

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