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Record W4401774259 · doi:10.5194/tc-18-3741-2024

On the relationship between <i>δ</i> O <sub>2</sub> ∕N <sub>2</sub> variability and ice sheet surface conditions in Antarctica

2024· article· en· W4401774259 on OpenAlexaff
Romilly Harris Stuart, Amaëlle Landais, Laurent Arnaud, Christo Buizert, Émilie Capron, Marie Dumont, Quentin Libois, Robert Mulvaney, Anaïs Orsi, Ghislain Picard, Frédéric Prié, Jeffrey P. Severinghaus, Barbara Stenni, Patricia Martinerie

Bibliographic record

Venue˜The œcryosphere · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
FundersHORIZON EUROPE Marie Sklodowska-Curie ActionsH2020 European Research CouncilAgence Nationale de la RechercheEuropean Commission
KeywordsIce sheetGeologyAntarctic ice sheetSurface (topology)Physical geographyCryosphereClimatologyGeomorphologyGeographySea iceGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract. While the processes controlling pore closure are broadly understood, the physical mechanisms driving the associated elemental fractionation remains ambiguous. Previous studies have shown that the pore closure process leads to a depletion in small-sized molecules (e.g. H2, O2, Ar, Ne, He) in ice core bubbles relative to larger-sized molecules like N2. This size-dependent fractionation, identified using ice core δ(O2/N2) records, exhibits a clear anti-correlation with local summer solstice insolation, making δ(O2/N2) a valuable ice core dating tool. Mechanisms controlling this relationship are attributed to the physical properties of deep firn. In this study, we compile δ(O2/N2) records from 15 polar ice cores and show a new additional link between δ(O2/N2) and local surface temperature and/or accumulation rate. Using the Crocus snowpack model, we perform sensitivity tests to identify the response of near-surface snow properties to changes in insolation intensity, accumulation rate, and air temperature. These tests support a mechanism linked to firn grain size, such that the larger the grain size for a given density, the stronger the pore closure fractionation and, hence, the lower the δ(O2/N2) values archived in the ice. Based on both snowpack model outputs and data compilation, our findings suggest that local accumulation rate and temperature should be considered when interpreting δ(O2/N2) as a local insolation proxy.

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 score0.013
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.233
Teacher spread0.207 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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