New technique for high-precision, simultaneous measurements of CH <sub>4</sub> , N <sub>2</sub> O and CO <sub>2</sub> concentrations; isotopic and elemental ratios of N <sub>2</sub> , O <sub>2</sub> and Ar; and total air content in ice cores by wet extraction
Bibliographic record
Abstract
Abstract. Air in polar ice cores provides unique information on past climatic and atmospheric changes. We developed a new method combining wet extraction, gas chromatography and mass spectrometry for high-precision, simultaneous measurements of eight air components (CH4, N2O and CO2 concentrations; δ15N, δ18O, δO2∕N2 and δAr∕N2; and total air content) from an ice-core sample of ∼ 60 g. The ice sample is evacuated for ∼ 2 h and melted under vacuum, and the released air is continuously transferred into a sample tube at 10 K within 10 min. The air is homogenized in the sample tube overnight at room temperature and split into two aliquots for mass spectrometric and gas chromatographic measurements. Care is taken to minimize (1) contamination of greenhouse gases by using a long evacuation time, (2) consumption of oxygen during sample storage by a passivation treatment on sample tubes, and (3) fractionation of isotopic ratios with a long homogenization time for splitting. Precision is assessed by analyzing standard gases with artificial ice and duplicate measurements of the Dome Fuji and NEEM ice cores. The overall reproducibility (1 SD) of duplicate ice-core analyses are 3.2 ppb, 2.2 ppb and 2.9 ppm for CH4, N2O and CO2 concentrations; 0.006 ‰, 0.011 ‰, 0.09 ‰ and 0.12 ‰ for δ15N, δ18O, δO2∕N2 and δAr∕N2; and 0.63 mLSTP kg−1 for total air content, respectively. Our new method successfully combines the high-precision, small-sample and multiple-species measurements, with a wide range of applications for ice-core paleoenvironmental studies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".