A meta‐analysis of oceanic DMS and DMSP cycling processes: Disentangling the summer paradox
Bibliographic record
Abstract
Abstract The biogenic volatile compound dimethylsulfide (DMS) is produced in the ocean mainly from the ubiquitous phytoplankton osmolyte dimethylsulfoniopropionate (DMSP). In the upper mixed layer, DMS concentration and the daily averaged solar irradiance are roughly proportional across latitudes and seasons. This translates into a seasonal mismatch between DMS and phytoplankton biomass at low latitudes, termed the “DMS summer paradox,” which remains difficult to reproduce with biogeochemical models. Here we report on a global meta‐analysis of DMSP and DMS cycling processes and their relationship to environmental factors. We show that DMS seasonality reflects progressive changes in a short‐term dynamic equilibrium, set by the quotient between gross DMS production rates and the sum of biotic and abiotic DMS consumption rate constants. Gross DMS production is the principal driver of DMS seasonality, due to the synergistic increases toward summer in two of its underlying factors: phytoplankton DMSP content (linked to species succession) and short‐term community DMSP‐to‐DMS conversion yields (linked to physiological stress). We also show that particulate DMSP transformations (linked to grazing‐induced phytoplankton mortality) generally contribute a larger share of gross DMS production than dissolved‐phase DMSP metabolism. The summer paradox is amplified by a decrease in microbial DMS consumption rate constants toward summer. However, this effect is partially compensated by a concomitant increase in abiotic DMS loss rate constants. Besides seasonality, we identify consistent covariation between key sulfur cycling variables and trophic status. These findings should improve the modeling projections of the main natural source of climatically active atmospheric sulfur.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".