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Record W2580640566 · doi:10.1017/jog.2016.138

Asynchronous behavior of outlet glaciers feeding Godthåbsfjord (Nuup Kangerlua) and the triggering of Narsap Sermia's retreat in SW Greenland

2017· article· en· W2580640566 on OpenAlexaff
R. J. Motyka, Ryan Cassotto, Martin Truffer, Kristian K. Kjeldsen, D. van As, Niels J. Korsgaard, M. A. Fahnestock, Ian M. Howat, Peter L. Langen, John Mortensen, Kunuk Lennert, Søren Rysgaard

Bibliographic record

VenueJournal of Glaciology · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of ManitobaUniversity of Ottawa
FundersOffice of Polar ProgramsU.S. Geological SurveyNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsGeologyGlacierPhysical geographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

ABSTRACT We assess ice loss and velocity changes between 1985 and 2014 of three tidewater and five-land terminating glaciers in Godthåbsfjord (Nuup Kangerlua), Greenland. Glacier thinning accounted for 43.8 ± 0.2 km 3 of ice loss, equivalent to 0.10 mm eustatic sea-level rise. An additional 3.5 ± 0.3 km 3 was lost to the calving retreats of Kangiata Nunaata Sermia (KNS) and Narsap Sermia (NS), two tidewater glaciers that exhibited asynchronous behavior over the study period. KNS has retreated 22 km from its Little Ice Age (LIA) maximum (1761 AD), of which 0.8 km since 1985. KNS has stabilized in shallow water, but seasonally advects a 2 km long floating tongue. In contrast, NS began retreating from its LIA moraine in 2004–06 (0.6 km), re-stabilized, then retreated 3.3 km during 2010–14 into an over-deepened basin. Velocities at KNS ranged 5–6 km a −1 , while at NS they increased from 1.5 to 5.5 km a −1 between 2004 and 2014. We present comprehensive analyses of glacier thinning, runoff, surface mass balance, ocean conditions, submarine melting, bed topography, ice mélange and conclude that the 2010–14 NS retreat was triggered by a combination of factors but primarily by an increase in submarine melting.

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 score0.014
Threshold uncertainty score0.231

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 studies0.0000.001
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 head0.023
GPT teacher head0.253
Teacher spread0.230 · 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

Citations60
Published2017
Admission routes1
Has abstractyes

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