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Record W3124321507 · doi:10.3390/min11020120

Thermo-Mechanical Regime of the Greenland Ice Sheet and Erosion Potential of the Crystalline Bedrock

2021· article· en· W3124321507 on OpenAlexafffund
Zhenze Li, T.S. Nguyen

Bibliographic record

VenueMinerals · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsCanadian Nuclear Safety Commission
FundersCanadian Nuclear Safety Commission
KeywordsGeologyBedrockErosionIce sheetMeltwaterIce-sheet modelGeomorphologyIce streamGlacierGlacial periodGreenland ice sheetClimatologyCryosphereSea ice

Abstract

fetched live from OpenAlex

Past glaciation is known to have caused a substantial morphological change to high latitude regions of the northern hemisphere. In the assessment of the long-term performance of deep geological repositories for radioactive wastes, future glaciation is a critical factor to take into consideration. This study develops a thermal-mechanical model to investigate ice sheet thermal evolution and the impact on bedrock erosion. The model is based on comprehensive field data resulting from international collaborative research on the Greenland Analogue Project. The ice sheet model considers surface energy balance and basal heat flux, as well as the temperature-dependent flow of ice that follows Glen’s law. The ice-bedrock interface is treated with a mechanical contact model, which solves the relative velocity and predicts the abrasional erosion and meltwater flow erosion. The numerical model is calibrated with measured temperature profiles and surface velocities at different locations across the glacier cross-section. The erosion rate is substantially larger near the glacier edge, where channel flow erosion becomes predominant. The abrasional erosion rate is averaged at 0.006 mm/a, and peaks at regions near the ridge divide. The mean meltwater flow erosion rate in the study area is estimated to be about 0.12 mm/a for the melted base region.

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 score0.472
Threshold uncertainty score0.598

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 head0.016
GPT teacher head0.204
Teacher spread0.188 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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