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Record W4247860833 · doi:10.5194/tcd-8-2611-2014

Winter speed-up of quiescent surge-type glaciers in Yukon, Canada

2014· preprint· en· W4247860833 on OpenAlexaboutno aff
Takahiro Abe, Masato Furuya

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceMinistry of Economy, Trade and IndustryJapan Aerospace Exploration AgencyNational Aeronautics and Space Administration
KeywordsSurgeGlacierMeltwaterGeologyGlacial periodClimatologySea level riseGlacier ice accumulationPhysical geographyIce streamClimate changeOceanographyGeomorphologyCryosphereGeographySea ice

Abstract

fetched live from OpenAlex

Abstract. Glacier surge is known to often initiate in winter, but the mechanisms remain unclear in light of the summer speed-up at normal glaciers. We examined spatial-temporal changes in the ice velocity of surge-type glaciers near the border of Alaska and Yukon, and found significant upstream accelerations from fall to winter, regardless of surging episodes. Moreover, whereas the summer speed-up was observed downstream, the winter speed-up propagated from upstream to downglacier. Given the absence of upstream surface meltwater input in winter, we speculate the presence of water storages near the base that do not directly connect to the surface but can promote basal sliding through increased water pressure as winter approaches. Our findings have implications for modeling of glacial hydrology in winter time, and its link to glacier dynamics and subglacial erosion.

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)0.001
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 score0.077
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.023
GPT teacher head0.216
Teacher spread0.193 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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