MétaCan
Menu
Back to cohort
Record W4313332706 · doi:10.1029/2022gl098009

Seasonal Acceleration of Petermann Glacier, Greenland, From Changes in Subglacial Hydrology

2022· article· en· W4313332706 on OpenAlexaff
Shivani Ehrenfeucht, Mathieu Morlighem, Eric Rignot, Christine F. Dow, J. Mouginot

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Waterloo
FundersAgence Nationale de la Recherche
KeywordsGlacierGeologyClimatologyFuture sea levelGreenland ice sheetIce sheetIce streamGeomorphologyHydrology (agriculture)CryosphereSea ice

Abstract

fetched live from OpenAlex

Abstract Petermann Glacier is a major outlet glacier of northern Greenland that drains a marine‐based basin vulnerable to destabilization from enhanced oceanic and atmospheric forcings. Using satellite radar interferometry data from the Sentinel‐1a/b missions, we observe a seasonal glacier acceleration of 15% in the summer, from 1,250 to 1,500 m/yr near the grounding line, but the physical drivers of this seasonality have not been elucidated. Here, we use a subglacial hydrology model coupled one‐way to an ice sheet model to evaluate the role of subglacial hydrology as a physical mechanism explaining the seasonal acceleration. We find excellent agreement between the observed and predicted velocity in terms of timing and magnitude with the addition of an applied lower limit on effective pressure of 6% of ice overburden pressure. We conclude that seasonal changes in subglacial hydrology are sufficient to explain the observed seasonal speed up of Petermann Glacier.

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 score0.074
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.051
GPT teacher head0.285
Teacher spread0.234 · 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

Citations38
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicCryospheric studies and observationsFrench-language works237,207