Marine carbonyl sulfide (OCS) and carbon disulfide (CS <sub>2</sub> ): a compilation of measurements in seawater and the marine boundary layer
Bibliographic record
Abstract
Abstract. Carbonyl sulfide (OCS) and carbon disulfide (CS2) are volatile sulfur gases that are naturally formed in seawater and exchanged with the atmosphere. OCS is the most abundant sulfur gas in the atmosphere, and CS2 is its most important precursor. They have attracted increased interest due to their direct (OCS) or indirect (CS2 via oxidation to OCS) contribution to the stratospheric sulfate aerosol layer. Furthermore, OCS serves as a proxy to constrain terrestrial CO2 uptake by vegetation. Oceanic emissions of both gases contribute a major part to their atmospheric concentration. Here we present a database of previously published and unpublished (mainly shipborne) measurements in seawater and the marine boundary layer for both gases, available at https://doi.org/10.1594/PANGAEA.905430 (Lennartz et al., 2019). The database contains original measurements as well as data digitalized from figures in publications from 42 measurement campaigns, i.e., cruises or time series stations, ranging from 1982 to 2019. OCS data cover all ocean basins except for the Arctic Ocean, as well as all months of the year, while the CS2 dataset shows large gaps in spatial and temporal coverage. Concentrations are consistent across different sampling and analysis techniques for OCS. The database is intended to support the identification of global spatial and temporal patterns and to facilitate the evaluation of model simulations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".