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Record W4285087333 · doi:10.1002/essoar.10511864.1

Moulin density controls the timing of peak pressurization within the Greenland Ice Sheet's subglacial drainage system

2022· preprint· en· W4285087333 on OpenAlexaff
Jessica Mejía, Jason Gulley, Celia Trunz, M. D. Covington, T. C. Bartholomaus, Charles Breithaupt, Surui Xie, Timothy H. Dixon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversité de Sherbrooke
FundersNational Science Foundation
KeywordsElectronic mailWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Links between hydrology and sliding of the Greenland Ice Sheet (GrIS) are poorly understood. Here, we monitored meltwater’s propagation through the entire glacial hydrologic system for catchments at different elevations by quantifying the lag cascade as daily meltwater pulses traveled through the supraglacial, englacial, and subglacial drainage systems. We found that meltwater’s residence time within supraglacial catchments-depending upon area, snow cover, and degree of channelization-controls the timing of peak moulin head, resulting in the two hour later peak observed at higher-elevations. Unlike at lower elevations where peak moulin head and sliding coincided, at higher elevations peak sliding lagged moulin head by ~2.8 hours. This delay was likely caused by the area’s lower moulin density, which required diurnal pressure oscillations to migrate further away from subglacial conduits to elicit the observed velocity response. These observations highlight the supraglacial drainage system’s control on coupling GrIS hydrology and sliding.

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)0.000
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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