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Record W2397139563 · doi:10.1017/aog.2016.7

Short-term velocity variations and sliding sensitivity of a slowly surging glacier

2016· article· en· W2397139563 on OpenAlexafffundabout
Gwenn E. Flowers, Alexander H. Jarosch, Patrick T. A. P. Belliveau, Lucas A. Fuhrman

Bibliographic record

VenueAnnals of Glaciology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSimon Fraser UniversityParks Canada
KeywordsGeologyGlacierSurgeFlow (mathematics)Anomaly (physics)Context (archaeology)ClimatologyTerm (time)Accumulation zoneGeodesyIce streamGeomorphologySea iceMechanicsCryospherePaleontology

Abstract

fetched live from OpenAlex

ABSTRACT We use daily surface velocities measured over several weeks in 2007 and 2008 on a slowly surging glacier in Yukon, Canada, to examine the ordinary melt-season dynamics in the context of the ongoing surge. Horizontal velocities within and just below the ~1.5 km-long zone of fastest flow, where the surge is occurring, are often correlated during intervals of low melt. This correlation breaks down during melt events, with the lower reaches of the fast-flow zone responding first. Velocity variability in this lower reach is most highly correlated with melt; velocities above and below appear to respond at least as strongly to the velocity variations of this reach as to local melt. GPS height records are suggestive of ice/bed separation occurring in the fast-flow zone but not below it, pointing to a hydrological cause for the short-term flow variability in the surging region. Independent velocity measurements over 6 years show a maximum July flow anomaly coincident with the location most responsive to melt. Results from a simple model of dashpots and frictional elements lend support to the hypothesis that this zone partly drives the dynamics of the ice above and below it. We speculate that the slow surge may enhance glacier sensitivity to melt-season processes, including short-term summer sliding events.

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)0.001
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 score0.046
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.067
GPT teacher head0.282
Teacher spread0.215 · 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

Citations19
Published2016
Admission routes3
Has abstractyes

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