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Record W2097156483 · doi:10.1029/2011gl048491

An increase in crevasse extent, West Greenland: Hydrologic implications

2011· article· en· W2097156483 on OpenAlexfundno aff
William Colgan, Konrad Steffen, W. Scott McLamb, W. Abdalati, Harihar Rajaram, R. J. Motyka, Thomas Phillips, Robert S. Anderson

Bibliographic record

VenueGeophysical Research Letters · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsCrevasseGeologyPhysical geographyClimatologyEarth scienceEnvironmental scienceGeomorphologyGeography

Abstract

fetched live from OpenAlex

[1] We compare high-resolution 1985 and 2009 imagery to assess changes in crevasse extent in the Sermeq Avannarleq ablation zone, West Greenland. The area occupied by crevasses >2 m wide significantly increased (13 ± 4%) over the 24-year period. This increase consists of an expansion of existing crevasse fields, and is accompanied by widespread changes in crevasse orientation (up to 45°). We suggest that a combination of ice sheet thinning and steepening are responsible for the increase in crevasse extent. We examine the potential impact of this change on the hydrology of the ice sheet. We provide a first-order demonstration that moulin-type drainage is more efficient in transferring meltwater fluctuations to the subglacial system than crevasse-type drainage. As enhanced basal sliding is associated with meltwater “pulses”, an increase in crevasse extent can therefore be expected to result in a net decrease in basal sliding sensitivity. An increase in crevasse extent may also accelerate cryo-hydrologic warming and enhance surface ablation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.092
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.091
GPT teacher head0.312
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations98
Published2011
Admission routes1
Has abstractyes

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