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Record W2730317824 · doi:10.1002/2017gl074081

Ice‐dammed lake drainage in west Greenland: Drainage pattern and implications on ice flow and bedrock motion

2017· article· en· W2730317824 on OpenAlexaff
Kristian K. Kjeldsen, Shfaqat Abbas Khan, Anders Anker Bjørk, Karina Nielsen, J. Mouginot

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Ottawa
FundersDanmarks Frie Forskningsfond
KeywordsGeologyBedrockDrainageIce streamIce sheetGeomorphologyGroenlandiaOceanographyHydrology (agriculture)CryosphereSea iceGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Ice‐dammed lakes drain frequently in Greenland, but the impacts of these events differ between sites. Here we study the quasi‐cyclic behavior of the ~40 km2 Lake Tininnilik in west Greenland and its impact on ice flow and crustal deformation. Data reveal rapid drainage of 1.83 ± 0.17 km3 of water in less than 7 days in 2010, leading to a speedup of the damming glacier, and an instantaneous modeled elastic bedrock uplift of 18.6 ± 0.1 mm confirmed by an independent lakeside GPS record. Since ice‐dammed lakes are common on Greenland, our results highlight the importance of including other sources of surface loading in addition to ice mass change, when assessing glacial isostatic adjustment or elastic rebound using geodetic data. Moreover, the results illustrates a linkage between subglacial discharge and ice surface velocity, important for assessing ice flux, and thus mass balance, in a future warming climate.

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 score0.170
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.047
GPT teacher head0.299
Teacher spread0.253 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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