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Record W4394854675 · doi:10.1017/jog.2024.32

Repeated subglacial jökulhlaups in northeastern Greenland revealed by CryoSat

2024· article· en· W4394854675 on OpenAlexafffund
Laurence Gray, David Burgess, Luke Copland, Christine F. Dow, Xavier Fettweis, David Fisher, William Kochtitzky, Wesley Van Wychen, James Zheng

Bibliographic record

VenueJournal of Glaciology · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of WaterlooGeological Survey of CanadaNatural Resources CanadaUniversity of Ottawa
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsEuropean Space AgencyNational Science Foundation
KeywordsGeologyOutflowElevation (ballistics)GlacierIce capsOceanographyPhysical geographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract Surface height changes above three previously undetected subglacial lakes in northeastern Greenland are documented using CryoSat, DEMs and ICESat-2. Between 7 February and 6 March 2012, the central ice region (22.6 km 2 ) above the largest lake dropped by ~37 m followed by a further drop of 12 m in the following 29 days. This implies a subglacial water outflow, or jökulhlaup , of at least 1 km 3 at rates of hundreds of cubic meters per second. A comparable outflow occurred again between 23 July and 15 September 2019, with smaller outflows in the fall of 2014 and 2016. In contrast, a second smaller subglacial lake at a higher elevation had two subglacial outbursts of ~0.3 km 3 in 2012 and 2019 but the lake filling was gradual and not strongly seasonal or episodic. Water remained in both lakes after the outflows but this may not be the case for the third smallest and lowest subglacial lake. While there appears to be some hydrological link between the three lakes, the flux of water moving under the ice in this area appears to be larger than would be expected from local summer melt. However, the source of the excess water remains uncertain.

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 score0.069
Threshold uncertainty score0.856

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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