Modeling analysis of the effect of iron enrichment on dimethyl sulfide dynamics in the NE Pacific (SERIES experiment)
Bibliographic record
Abstract
The large‐scale iron enrichment conducted in the NE Pacific during the Subarctic Ecosystem Response to Iron Enrichment Study (SERIES) triggered a phytoplankton bloom dominated successively by nanophytoplankton and large diatoms. During the first 14 days, surface dimethyl sulfide (DMS) levels increased both inside (up to 22 nmol L−1) and outside (up to 19 nmol L−1) the patch, with no consistent Fe effect. Later, DMS concentrations became sixfold lower inside the patch than outside. In this study, we used a DMS budget module embedded in a one‐dimensional ocean turbulence model to investigate the contribution of the interacting physical, photochemical, and biological processes to this particular DMS response. Temporal variations in biological net DMS production were reconstructed using an inverse modeling approach. Our results show that short‐term (days) variations in both the physical processes (i.e., turbulent mixing and ventilation) and the biological cycling of DMS are needed to explain the time evolution of DMS concentrations both outside and inside the Fe‐enriched patch. The biological net DMS production was generally high (up to 0.35 nmol L−1 h−1) and comparable outside and inside the patch during the first 10 days, corresponding to the observed accumulation of DMS inside and outside the patch. Later, it became negative (net DMS biological consumption) inside the patch, suggesting a change in dimethylsulfoniopropionate bacterial metabolism. This study stresses the importance of short‐term variations in biological processes and their sensitivity to the physical environment in shaping the DMS response to iron enrichment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".