Application of purge and trap‐atmospheric pressure chemical ionization‐tandem mass spectrometry for the determination of dimethyl sulfide in seawater
Bibliographic record
Abstract
Abstract We describe a method for measuring trace concentrations of dimethyl sulfide (DMS) in seawater using a commercial tandem mass spectrometer configured for atmospheric pressure chemical ionization (PT‐APCI‐MS/MS), coupled with a custom‐built purge and trap gas extraction system. DMS was ionized through proton transfer, generating abundant [M + H]+ ions. The semiautomated method analyzes samples in under 6 min, and is capable of processing up to 10 samples in a single batch. A detection limit of 0.9 pmol L−1 was determined for the analysis of 5 mL sample volumes, with a precision of 3.9% between replicates. Practical performance was evaluated during two oceanographic research cruises within the coastal waters around Vancouver Island, British Columbia. To demonstrate method utility, a series of DMS depth profiles were obtained along two transects extending from the west coast of Vancouver Island into deep water off the continental shelf. Additional depth profile sampling was conducted in Saanich Inlet, a coastal anoxic fjord with active chemotrophic sulfur cycling. This method enabled us to capture the deep‐water accumulation of subnanomolar DMS in the anoxic water of Saanich Inlet, providing evidence of cryptic sulfur cycling. The method was also leveraged to facilitate stable isotope rate measurement experiments, in which the consumption of isotopically labeled DMS, dimethylsulfoxide, and dimethylsulfoniopropionate tracers was monitored in the low picomolar range. These measurements enable metabolic rate determinations using low‐level tracer additions that do not perturb in situ microbial activity. Our sensitive, high throughput method helps to improve understanding of the natural marine cycling of volatile sulfur compounds.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".