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Record W2594263116 · doi:10.5194/tc-11-669-2017

Liquid water content in ice estimated through a full-depth ground radar profile and borehole measurements in western Greenland

2017· article· en· W2594263116 on OpenAlexfundno aff
Joel Brown, J. T. Harper, N. F. Humphrey

Bibliographic record

Venue˜The œcryosphere · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersOffice of Polar ProgramsNuclear Waste Management OrganizationNational Science Foundation
KeywordsGeologyIce streamGlacierIce sheetBoreholeGlacier morphologyGeomorphologySea ice thicknessCryosphereClimatologySea icePaleontology

Abstract

fetched live from OpenAlex

Abstract. Liquid water content (wetness) within glacier ice is known to strongly control ice viscosity and ice deformation processes. Little is known about wetness of ice on the outer flanks of the Greenland Ice Sheet, where a temperate layer of basal ice exists. This study integrates borehole and radar surveys collected in June 2012 to provide direct estimates of englacial ice wetness in the ablation zone of western Greenland. We estimate electromagnetic propagation velocity of the ice body by inverting reflection travel times from radar data. Our inversion is constrained by ice thickness measured in boreholes and by positioning of a temperate–cold ice boundary identified in boreholes. Electromagnetic propagation velocities are consistent with a depth-averaged wetness of ∼ 0.5–1.1 %. The inversion indicates that wetness within the ice varies from < 0.1 % in an upper cold layer to ∼ 2.9–4.6 % in a 130–150 m thick temperate layer located above the glacier bed. Such high wetness should yield high rates of shear strain, which need to be accounted for in glacial flow models that focus on the ablation zone of Greenland. This high wetness also needs to be accounted for when determining ice thickness from radar measurements.

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 score0.395
Threshold uncertainty score0.935

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 studies0.0010.000
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 head0.103
GPT teacher head0.270
Teacher spread0.167 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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