Techniques for measuring carbon and oxygen isotope compositions of atmospheric CO <sub>2</sub> via isotope ratio mass spectrometry
Bibliographic record
Abstract
Measuring the stable isotope compositions of atmospheric CO 2 is common in earth and atmospheric sciences, and various analytical methods have been developed utilizing continuous‐flow (CF) or dual‐inlet (DI) isotope ratio mass spectrometry (IRMS). Air is typically collected via passive, manual, or automated collection methods and the volume of the air sample ranges from 10 to 300 mL for CF‐IRMS to >1 L for DI‐IRMS to yield a measurable amount of atmospheric CO 2 gas. It has been determined that the integrity of vials and flasks for air sample storage can be compromised after 3 days of air collection for δ 13 C values and within 10 hours for δ 18 O values. Air samples must be purified after collection to remove constituents of air, such as Ar, O 2 , N 2 , N 2 O, and water vapor, to avoid isobaric interferences during mass spectrometric measurement. Purification is generally undertaken by utilizing commercial or custom‐made preconcentration devices, the blanking method for CF‐IRMS, or an offline/online cryogenic separation using a vacuum line for DI‐IRMS. Ambient N 2 O is a component of air that may affect analytical results and thus must either be corrected for or be removed using a gas chromatographic column. In some cases, water is removed during air collection by using a common chemical desiccant, magnesium perchlorate (Mg(ClO 4 ) 2 ), or by a dry ice/alcohol mixture (−78°C). Lastly, a linearity issue for IRMS due to the low amount of purified CO 2 from a typical ambient air sample must be considered. In general, analytical precisions of 0.02–0.21‰ and 0.04–0.34‰ for CF‐IRMS and 0.01–0.02‰ and 0.01–0.02‰ for DI‐IRMS are expected for δ 13 C and δ 18 O measurements, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".