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Record W2793066906 · doi:10.1002/2017jf004409

Dynamics of Active Subglacial Lakes in Recovery Ice Stream

2018· article· en· W2793066906 on OpenAlexafffund
Christine F. Dow, Mauro Werder, G. S. Babonis, Sophie Nowicki, R. T. Walker, B. M. Csathó, Mathieu Morlighem

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Sleep FoundationNational Aeronautics and Space Administration
KeywordsGeologyHydrology (agriculture)DrainageIce streamGeomorphologyCryosphereSea iceOceanography

Abstract

fetched live from OpenAlex

Recovery Ice Stream has a substantial number of active subglacial lakes that are observed, with satellite altimetry, to grow and drain over multiple years. These lakes store and release water that could be important for controlling the velocity of the ice stream. We apply a subglacial hydrology model to analyze lake growth and drainage characteristics together with the simultaneous development of the ice stream hydrological network. Our outputs produce a good match between modeled lake location and those identified using satellite altimetry for many of the lakes. The modeled subglacial system demonstrates development of pressure waves that initiate at the ice stream neck and transit to within 100 km of the terminus. These waves alter the hydraulic potential of the ice stream and encourage growth and drainage of the subglacial lakes. Lake drainage can cause large R-channels to develop between basal overdeepenings that persist for multiple years. The pressure waves, along with lake growth and drainage rates, do not identically repeat over multiple years, due to basal network development. This suggests that the subglacial hydrology of Recovery Ice Stream is influenced by regional drainage development on the scale of hundreds of kilometers rather than local conditions over tens of kilometers.

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)0.001
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 score0.146
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.310
Teacher spread0.269 · 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

Citations39
Published2018
Admission routes2
Has abstractyes

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