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Record W1986273942 · doi:10.1130/g19795.1

Cosmogenic nuclides 10Be and 26Al imply limited Antarctic Ice Sheet thickening and low erosion in the Shackleton Range for >1 m.y.

2004· article· en· W1986273942 on OpenAlexaff
Christopher J. Fogwill, Michael J. Bentley, David E. Sugden, Andrew Kerr, Peter W. Kubik

Bibliographic record

VenueGeology · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsInstitute of Particle Physics
FundersNatural Environment Research Council
KeywordsCosmogenic nuclideCitationIconGeologyArchaeologyPhysical geographyGeographyHistoryOceanographyLibrary sciencePhysicsAstrophysicsComputer science

Abstract

fetched live from OpenAlex

Concentrations of the cosmogenic nuclides <sup>10</sup>Be and <sup>26</sup>Al on bedrock surfaces in the Shackleton Range, Antarctica, indicate minimum exposure ages between 3.0 ± 0.3 and 1.16 ± 0.10 Ma. The isotope data indicate that the maximum long-term erosion rate is 0.10-0.35 m/m.y., and the ratios suggest no prolonged periods of burial by cold-based ice. The findings are important because of the location of the massif close to the junction of the East Antarctic Ice Sheet and the landward extent of the Filchner Ice Shelf. These results point to three conclusions. First, the massif has not been overridden by the East Antarctic Ice Sheet during the late Quaternary. Rather, moraines 200-340 to above outlet glaciers are likely to represent the maximum thickening of the Filchner-Ronne Ice Shelf during the Quaternary. Second, the high radionuclide concentrations on bedrock surfaces, one of which is striated, suggest that some glacial landforms were created in at least Pliocene or more likely Miocene time. Third, the exceptionally low erosion rates imply that the modern cold, arid climate has persisted for millions of years. These findings provide evidence of old, stable landscapes over a wider area of Antarctica than the McMurdo Dry Valleys.

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 score0.022
Threshold uncertainty score0.995

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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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

Citations56
Published2004
Admission routes1
Has abstractyes

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