Measurement of <scp>DMS</scp>, <scp>DMSO</scp>, and <scp>DMSP</scp> in natural waters by automated sequential chemical analysis
Bibliographic record
Abstract
Abstract Dimethyl sulfide (DMS), dimethylsulfoniopropionate (DMSP), and dimethyl sulfoxide (DMSO) are key components of the marine sulfur cycle. The concentrations of these compounds exhibit large spatial and temporal variability in the surface ocean, creating a need for high resolution sampling. Existing automated underway measurement systems for DMS do not measure DMSP or DMSO, so their spatial variability is less well‐characterized. We present an accurate and robust method for the automated, high throughput sampling and measurement of DMS, DMSO, and DMSP (DMS/O/P) in a single water sample. The method is based on a three‐step sequence of purge and trap gas chromatography, where DMS analysis is followed by the enzymatic reduction of DMSO to DMS and the alkaline hydrolysis of DMSP to DMS. The system, which we call the Organic Sulfur Sequential Chemical Analysis Robot (OSSCAR), includes automated calibrations and blank determinations. OSSCAR can be used as a front‐end system for any sulfur detector and is suited for continuous underway analysis or the measurement of discrete water samples. The system described here has a minimum detection limit of ∼ 0.1 nM of DMS/O/P in a 2.5 mL sample. Assessment of liquid standards and intercalibration against independent analytical systems demonstrate good precision and accuracy of our method. Shipboard analysis of surface water DMS/O/P concentrations on a transect from Ocean Station Papa (50°N, 145°W) to Vancouver Island demonstrates the utility of OSSCAR for mapping variability in reduced sulfur compounds across dynamic and contrasting oceanographic conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".