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Record W2160712088 · doi:10.3189/172756404781814339

Sulfur isotopic measurements from a West Antarctic ice core: implications for sulfate source and transport

2004· article· en· W2160712088 on OpenAlexaff
L. E. Pruett, K. J. Kreutz, Moire A. Wadleigh, Paul A. Mayewski, Andrei V. Kurbatov

Bibliographic record

VenueAnnals of Glaciology · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsMemorial University of Newfoundland
FundersOffice of Polar ProgramsNational Science Foundation
KeywordsIce coreFirnGeologyVolcanoSulfateSulfurGlacierSulfur cycleCyclingDeposition (geology)Physical geographyAtmospheric sciencesOceanographyStructural basinGeomorphologyPaleontologyChemistryGeography

Abstract

fetched live from OpenAlex

Abstract Measurements of δ 34 S covering the years 1935–76 and including the 1963 Agung (Indonesia) eruption were made on a West Antarctic firn core, RIDSA (78.73˚ S, 116.33˚ W; 1740ma.s.l.), and results are used to unravel potential source functions in the sulfur cycle over West Antarctica. The δ 34 S values of SO42– range from 3.1‰ to 9.9‰. These values are lower than those reported for central Antarctica, from near South Pole station, of 9.3–18.1‰ (Patris and others, 2000). While the Agung period is isotopically distinct at South Pole, it is not in the RIDSA dataset, suggesting differences in the source associations for the sulfur cycle between these two regions. Given the relatively large input of marine aerosols at RIDSA (determined from Na + data and the seasonal SO42– cycle), there is likely a large marine biogenic SO42– influence. The δ 34 S values indicate, however, that this marine biogenic SO42–, with a well-established δ 34 S of 18‰, is mixing with SO42– that has extremely negative δ 34 S values to produce the measured isotope values in the RIDSA core. We suggest that the transport and deposition of stratospheric SO42– in West Antarctica, combined with local volcanic input, accounts for the observed variance in δ 34 S values.

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 score0.005
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.098
GPT teacher head0.293
Teacher spread0.195 · 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

Citations30
Published2004
Admission routes1
Has abstractyes

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