Local climate influences δO2/N2 variability in ice core records
Bibliographic record
Abstract
Orbital dating using δO2/N2 records is a powerful tool for constructing ice core chronologies in deep ice cores due a widely observed anti-correlation with summer solstice insolation (SSI). While understood to be linked to near-surface snow metamorphism, the physical mechanisms driving this process remain poorly constrained and the role of local accumulation rate and temperature have been scarcely considered. We primarily present the results of our new study which uses a compilation of records from 14 ice cores to show a significant dependence of mean δO2/N2 on local accumulation rate and temperature. Using EPICA Dome C as a case study, we then show that during rapid climatic changes, an accumulation/temperature signal may be superimposed on top of the SSI signal and therefore should be accounted for when using peak-matching techniques for future dating of deep ice cores, such as the EPICA or Beyond EPICA cores.Further to our study, we include new δO2/N2data measured in shallow, bubbly ice just below close-off from two newly drilled firn cores at sites with distinct close-off conditions; D-47 and Little Dome C (the Beyond EPICA site), which support our findings. Moreover, thanks to parallel firn air pumping campaigns, overlapping data from open and closed porosity at these two sites promise greater insight into the mechanisms driving close-off fractionation.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".