Bibliographic record
Abstract
Abstract. Processes controlling pore closure are broadly understood yet defining the physical mechanisms controlling associated elemental fractionation remains ambiguous. Previous studies have shown that the pore closure process leads to a decrease in concentration of small-size molecules (e.g., H2, O2, Ar, Ne, He) in the trapped bubbles. Ice core δ(O2/N2) records – the ratio of O2 to N2 molecules in bubbles trapped in ice cores relative to the atmosphere – are therefore depleted owing to this O2 loss and show a clear link with local summer solstice insolation making it a useful dating tool. In this study, we compile δ(O2/N2) records from 14 polar ice cores and show a new link between δ(O2/N2) and local surface temperature and/or accumulation rate, in addition to the influence of the summer solstice insolation. We argue that both local climate-driven and insolation forcings are linked to the modulation of snow physical properties near the surface. Using the Crocus snowpack model, we perform sensitivity tests to identify the response of near-surface snow properties to changes in insolation, accumulation rate, and air temperature. These tests support a mechanisms linked to snow grain size, such that the larger the grain size for a given density, the stronger the pore closure fractionation, and hence, lower δ(O2/N2) values. Our findings suggest that local accumulation rate and temperature should be considered when interpreting δ(O2/N2) as an 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.582 | 0.410 |
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".