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Record W4404287387 · doi:10.1139/as-2024-0024

Dynamic LIA advances hastened the demise of small valley glaciers in central Svalbard

2024· article· en· W4404287387 on OpenAlexvenueno aff
Erik Schytt Mannerfelt, Andy Hodson, Lena Håkansson, Harold Lovell

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsDemiseGlacierGeologyPhysical geographyGeographyGeomorphologyPolitical science

Abstract

fetched live from OpenAlex

Most small land-terminating glaciers in Svalbard have experienced large recession since the Little Ice Age (LIA) and today are thin, cold, and largely inactive. This likely contrasts to their LIA conditions, but the observational record from that time is sparse. We investigate the evolution of five small glaciers in central Nordenskiöld Land, Svalbard, from the LIA to 2019. Photogrammetric reconstructions and ground penetrating radar are used to reconstruct their geometric changes since 1936, and historical observation, photographs, and geomorphological mapping extend this history to before the 1900s. Our results show that from 1936 to 2019, the study glaciers on average lost 49.6% of their area and 77.4% ± 7.7% of their volume, with the greatest volume loss at Scott Turnerbreen of 91% ± 5%. Four out of these five glaciers strongly indicate a history of surge-like advances near the end of the LIA within one or two decades, and the rate of subsequent mass loss seems connected to their previous dynamics. This apparent switch to high activity during a period of rapid climatic change, could have implications for our understanding of past and future glacier evolution; climate change and highly dynamic glacier responses may be more connected than previously thought.

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)0.000
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 score0.100
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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