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Record W2986683100 · doi:10.1017/jog.2019.80

Meltwater percolation, impermeable layer formation and runoff buffering on Devon Ice Cap, Canada

2019· article· en· W2986683100 on OpenAlexaffabout
David W. Ashmore, Douglas Mair, David Burgess

Bibliographic record

VenueJournal of Glaciology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsNatural Resources Canada
FundersUniversity of LiverpoolNational Science Foundation
KeywordsMeltwaterGeologySnowpackFirnSea iceMelt pondPercolation (cognitive psychology)Ice streamArcticSnowArctic ice packGeomorphologyIce sheetSurface runoffAntarctic sea iceCryosphereClimatologyOceanography

Abstract

fetched live from OpenAlex

Abstract The retention of meltwater in the accumulation area of the Greenland ice sheet and other Arctic ice masses buffers their contribution to sea level change. However, sustained warming also results in impermeable ice layers or ‘ice slabs’ that seal the underlying pore space. Here, we use a 1-D, physically based, high-resolution model to simulate the surface mass balance (SMB), percolation, refreezing, ice layer formation and runoff from across the high-elevation area of Devon Ice Cap, Canada, from 2001 to 2016. We vary the thickness of the ‘impermeable’ ice layer at which underlying firn becomes inaccessible to meltwater. Thick near-surface ice layers are established by an initial deep percolation, the formation of decimetre ice layers and the infilling of interleaving pore space. The cumulative SMB increases by 48% by varying impermeable layer thickness between 0.01 and 5 m. Within this range we identify narrower range (0.25–1 m) that can simulate both the temporal variability in SMB and the observed near-surface density structure. Across this range, cumulative SMB variation is limited to 6% and 45–49% of mass retention takes place within the annually replenished snowpack. Our results indicate cooler summers after intense mid-2000s warming have led to a partial replenishment of pore space.

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 score0.229
Threshold uncertainty score0.932

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 head0.011
GPT teacher head0.190
Teacher spread0.179 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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