On the relationship between <i>δ</i> O <sub>2</sub> ∕N <sub>2</sub> variability and ice sheet surface conditions in Antarctica
Bibliographic record
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. H 2 , O 2 , Ar, Ne, He) in ice core bubbles relative to larger-sized molecules like N 2 . 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".