Ecosystem modelling of the cycling of marine dimethylsulfide: a review of current approaches and of the potential for extrapolation to global scales
Bibliographic record
Abstract
There is rising interest from oceanic and atmospheric scientists in the potential role of dimethylsulphide (DMS) in regulating global climate. The increased availability of field observations of DMS and related compounds (DMS(P)) and of their transformation rates in the ocean has stimulated the development of ecosystem models of marine sulfur cycling. The models cover a wide range of complexity levels and spatial/temporal scales, from zero-dimensional local simulations spanning a few days to regional/global simulations driven by ocean general circulation models. The degree of complexity required to model DMS(P) dynamics, particularly the differentiation into phyto plankton species or groups, remains an important open question. First attempts to drive these models with vertically resolved turbulence models suggest interesting interactions between DMS(P) dynamics and fine-scale ocean mixing that can modify fluxes of DMS to the atmosphere. Recent models also bring into focus the strong affinities between the cycling of DMS(P) and that of dissolved organic carbon in the surface ocean. Formal parameter estimation techniques, which are increasingly used in ecosystem modelling of carbon and nitrogen dynamics, should play a stronger role in the development of DMS sulfur modelling. Extrapolation of DMS cycling and fluxes to the global scale presently relies largely on empirical approach. A semiempirical approach, based on a simple ecosystem model, is shown to reproduce gross features of the global distribution of DMS in the surface ocean. This shows promise for the continuing development of ecosystem models for global modelling of marine sulfur fluxes to the atmosphere.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".