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Record W4410143076 · doi:10.5194/tc-19-1725-2025

Speed-up, slowdown, and redirection of ice flow on neighbouring ice streams in the Pope, Smith, and Kohler region of West Antarctica

2025· article· en· W4410143076 on OpenAlexfundno aff
Heather L. Selley, Anna E. Hogg, Benjamin Davison, Pierre Dutrieux, Thomas Slater

Bibliographic record

Venue˜The œcryosphere · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersJapan Aerospace Exploration AgencyNatural Environment Research CouncilNASA HeadquartersUniversity of LeedsCanadian Space AgencyUK Research and InnovationNuclear Safety and Security CommissionEuropean Space AgencyNational Aeronautics and Space Administration
KeywordsSTREAMSSlowdownIce streamGeologyFlow (mathematics)Ice coreIce sheetLittle ice ageGlacierClimatologyPhysical geographyCryosphereOceanographySea iceGeographyGeomorphologyPhysicsMechanics

Abstract

fetched live from OpenAlex

Abstract. The ice streams feeding the Dotson and Crosson ice shelves are some of the fastest changing in West Antarctica. We use satellite observations to measure the change in ice speed and flow direction on eight ice streams in the Pope, Smith, and Kohler region of West Antarctica from 2005 to 2022. Seven ice streams have sped up at the grounding line, with the largest increase in ice speed at Smith West Glacier (87 %), whilst Kohler West Glacier has slowed by 10 %. We observe progressive redirection of ice flowlines from Kohler West into the more rapidly thinning and accelerating Kohler East Glacier, resulting in the deceleration of Kohler West Glacier and eastward migration of the ice divide between Dotson and Crosson ice shelves. These observations reveal previously undocumented impacts of spatially varying ice speed and thickness changes on flow direction and ice flux into downstream ice shelves, which may influence ice shelf and ice sheet mass change during the 21st century.

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 score0.180
Threshold uncertainty score0.994

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)0.0000.000

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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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