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Record W2594419865 · doi:10.1017/jog.2017.5

Quantifying supraglacial meltwater pathways in the Paakitsoq region, West Greenland

2017· article· en· W2594419865 on OpenAlexaff
Conrad P. Koziol, Neil Arnold, Allen Pope, William Colgan

Bibliographic record

VenueJournal of Glaciology · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsYork University
FundersUniversity of MinnesotaNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsMeltwaterGeologyGreenland ice sheetSurface runoffMelt pondDrainageHydrology (agriculture)GeomorphologyIce sheetGlacierPhysical geographyCryosphereIce streamOceanographySea ice

Abstract

fetched live from OpenAlex

ABSTRACT Increased summer ice velocities on the Greenland ice sheet are driven by meltwater input to the subglacial environment. However, spatial patterns of surface input and partitioning of meltwater between different pathways to the base remain poorly understood. To further our understanding of surface drainage, we apply a supraglacial hydrology model to the Paakitsoq region, West Greenland for three contrasting melt seasons. During an average melt season, crevasses drain ~47% of surface runoff, lake hydrofracture drains ~3% during the hydrofracturing events themselves, while the subsequent surface-to-bed connections drain ~21% and moulins outside of lake basins drain ~15%. Lake hydrofracture forms the primary drainage pathway at higher elevations (above ~850 m) while crevasses drain a significant proportion of meltwater at lower elevations. During the two higher intensity melt seasons, model results show an increase (~5 and ~6% of total surface runoff) in the proportion of runoff drained above ~1300 m relative to the melt season of average intensity. The potential for interannual changes in meltwater partitioning could have implications for how the dynamics of the ice sheet respond to ongoing changes in meltwater production.

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 score0.020
Threshold uncertainty score0.588

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 head0.104
GPT teacher head0.282
Teacher spread0.178 · 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

Citations57
Published2017
Admission routes1
Has abstractyes

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