Determination of the S Isotope Composition of Methanesulfonic Acid
Bibliographic record
Abstract
Sulfur (S) isotopes have been used to apportion the amount of biogenic and anthropogenic sulfate in remote environments, an important parameter that is used to model the global radiation budget. A key assumption in the apportionment calculations is that there is little isotope selectivity as reduced compounds such as dimethyl sulfide (DMS) are oxidized. This paper describes a method to determine, for the first time, the S isotope composition of methanesulfonic acid (MSA), the product of DMS oxidation. The isotope composition of MSA was measured directly by EA-IRMS and was used as an isotope reference for the method. Synthetic mixtures approximating the conditions expected for aerosol MSA samples were prepared to test this method. First, MSA solutions were measured alone and then in combination with MSA and SO4(2-). In synthetic mixtures, SO4(2-) was separated from MSA by precipitating it as BaSO4 prior to preparation of MSA for isotope analysis. The delta 34S value for MSA solutions was -2.6 per thousand (SD +/- 0.4 per thousand), which is not different from the delta 34S obtained from MSA filtrate after precipitating SO4(2-) from the mixture (-2.7 +/- 0.3 per thousand). However, these values are offset from direct EA-IRMS analysis of MSA used as the isotope reference by -1.1 +/- 0.2 per thousand, and this must be accounted for in reporting MSA measurements. The S isotope measurements using this method approach a limiting value above 300 microg of MSA. This is approximately equal to the MSA found in 20,000 m3 of air, assuming ambient concentrations of approximately 15 ng m(-3). Three samples of MSA from the Pacific Ocean measured using this technique have an average delta 34S value of +17.4 +/- 0.7 per thousand.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".