From cells to globe: approaching the dynamics of DMS(P) in the ocean at multiple scales
Bibliographic record
Abstract
Major advances in dimethylated sulfur research are being made by approaching its dynamics at multiple scales. At the molecular to cellular level, single-cell techniques in molecular biology allow us to identify the microbes involved in cycling of dimethylated sulfur. Also, we find that dimethylsulfoxide (DMSO) is as ubiquitous as dimethyl sulfoniopropionate (DMSP) in marine plankton, which supports the recent suggestion that both compounds are involved in coping with oxidative stress. At the community level, there is recent evidence for the role of DMSP as a major carrier in organic sulfur transfer and cycling through trophic levels, from phytoplankton to bacteria and to zooplankton through herbivore protozoans. As a consequence, the food web dynamics drive the oceanic emission of atmospheric sulfur. At the ecosystem level, the diverse and intricate effects of the physicochemical setting (light, wind, nutrients) on the oceanic cycling of dimethylated sulfur are being uncovered. A proposed shortcut to detailed understanding of the individual processes presents the depth of the surface mixed layer as the variable that integrates most of the environmental effects and serves for predicting dimethylsulfide (DMS) concentrations, even at the global ocean level. This opens the door to assessing the strength of the DMS biogeophysical system as a climate regulator.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".