Concentrations and cycling of DMS, DMSP, and DMSO in coastal and offshore waters of the Subarctic Pacific during summer, 2010‐2011
Bibliographic record
Abstract
Abstract Concentrations of dimethylsulfide (DMS), measured in the Subarctic Pacific during summer 2010 and 2011, ranged from ∼1 to 40 nM, while dissolved dimethylsulfoxide (DMSO) concentrations (range 13‐23 nM) exceeded those of dissolved dimethyl sulfoniopropionate (DMSP) (range 1.3–8.8 nM). Particulate DMSP dominated the reduced sulfur pool, reaching maximum concentrations of 100 nM. Coastal and off shore waters exhibited similar overall DMS concentration ranges, but sea‐air DMS fluxes were lower in the oceanic waters due to lower wind speeds. Surface DMS concentrations showed statistically significant correlations with various hydrographic variables including the upwelling intensity (r2 = 0.52, p < 0.001) and the Chlorophyll a/mixed layer depth ratio (r2 = 0.52, p < 0.001), but these relationships provided little predictive power at small scales. Stable isotope tracer experiments indicated that the DMSP cleavage pathway always exceeded the DMSO reduction pathway as a DMS source, leading to at least 85% more DMS production in each experiment. Gross DMS production rates were positively correlated with the upwelling intensity, while net rates of DMS production were significantly correlated to surface water DMS concentrations. This latter result suggests that our measurements captured dominant processes driving surface DMS accumulation across a coastal‐oceanic gradient.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".