New technique for high-precision, simultaneous measurements ofCH <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 various 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, δAr/N2 and total air content) from an ice core sample of ~60 g. The ice sample is evacuated for ~2 hours and melted under vacuum, and the released air is continuously transferred into a sample tube at 10 K within 10 minutes. The air is homogenized in the sample tube overnight at room temperature, and split into two aliquots for mass spectrometric and gas chromatographic measurements. Cares are taken to minimize contamination of greenhouse gases with long evacuation time, consumption of oxygen during sample storage by passivation treatment on sample tubes, and fractionation of isotopic ratios with long homogenization time for splitting. Precisions are assessed by analysing standard gases with artificial ice, and by duplicate measurements of the Dome Fuji and NEEM ice cores. The overall reproducibility (one standard deviation) from duplicate ice-core analyses are 3.2 ppb, 2.2 ppb and 3.1 ppm for CH4, N2O and CO2 concentrations, 0.006, 0.010, 0.09 and 0.12 ‰ for δ15N, δ18O, δO2/N2 and δAr/N2, and 0.67 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".