New methods for measuring atmospheric heavy noble gas isotope and elemental ratios in ice core samples
Bibliographic record
Abstract
Rationale The global ocean constitutes the largest heat buffer in the global climate system, but little is known about its past changes. The isotopic and elemental ratios of heavy noble gases (krypton and xenon), together with argon and nitrogen in trapped air from ice cores, can be used to reconstruct past mean ocean temperatures (MOTs). Here we introduce two successively developed methods to measure these parameters with a sufficient precision to provide new constraints on past changes in MOT. Methods The air from an 800‐g ice sample – containing roughly 80 mL STP air – is extracted and processed to be analyzed on two independent dual‐inlet isotope ratio mass spectrometers. The primary isotope ratios (δ 15 N, δ 40 Ar and δ 86 Kr values) are obtained with precisions in the range of 1 per meg (0.001‰) per mass unit. The three elemental ratio values δKr/N 2 , δXe/N 2 and δXe/Kr are obtained using sequential (non‐simultaneous) peak‐jumping, reaching precisions in the range of 0.1–0.3‰. Results The latest version of the method achieves a 30% to 50% better precision on the elemental ratios and a twofold better sample throughput than the previous one. The method development uncovered an unexpected source of artefactual gas fractionation in a closed system that is caused by adiabatic cooling and warming of gases (termed adiabatic fractionation) – a potential source of measurement artifacts in other methods. Conclusions The precisions of the three elemental ratios δKr/N 2 , δXe/N 2 and δXe/Kr – which all contain the same MOT information – suggest smaller uncertainties for reconstructed MOTs (±0.3–0.1°C) than previous studies have attained. Due to different sensitivities of the noble gases to changes in MOT, δXe/N 2 provides the best constraints on the MOT under the given precisions followed by δXe/Kr, and δKr/N 2 ; however, using all of them helps to detect methodological artifacts and issues with ice quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".