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Record W2791608688 · doi:10.1002/2017gl076251

Limited Impact of Subglacial Supercooling Freeze‐on for Greenland Ice Sheet Stratigraphy

2018· article· en· W2791608688 on OpenAlexafffund
Christine F. Dow, Nanna B. Karlsson, Mauro Werder

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of Canada
KeywordsGeologySupercoolingIce sheetGreenland ice sheetIce streamCryosphereFreezing pointDepth soundingIce formationGeomorphologyClimatologyGeophysicsAtmospheric sciencesOceanographySea iceMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract Large units of disrupted radiostratigraphy (UDR) are visible in many radio‐echo sounding data sets from the Greenland Ice Sheet. This study investigates whether supercooling freeze‐on rates at the bed can cause the observed UDR. We use a subglacial hydrology model to calculate both freezing and melting rates at the base of the ice sheet in a distributed sheet and within basal channels. We find that while supercooling freeze‐on is a phenomenon that occurs in many areas of the ice sheet, there is no discernible correlation with the occurrence of UDR. The supercooling freeze‐on rates are so low that it would require tens of thousands of years with minimal downstream ice motion to form the hundreds of meters of disrupted radiostratigraphy. Overall, the melt rates at the base of the ice sheet greatly overwhelm the freeze‐on rates, which has implications for mass balance calculations of Greenland ice.

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)0.001
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 score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.068
GPT teacher head0.331
Teacher spread0.263 · 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

Citations38
Published2018
Admission routes2
Has abstractyes

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