Microbial dimethylsulfoniopropionate (DMSP) dynamics along a natural iron gradient in the northeast subarctic Pacific
Bibliographic record
Abstract
We characterized the effect of an inshore‐offshore gradient in Fe in the northeast subarctic Pacific on the bacterioplankton and phytoplankton assemblages and on the microbial cycling of particulate and dissolved dimethylsulfoniopropionate (DMSPp and DMSPd) and dimethylsulfide (DMS). Averaged concentrations of total dissolved Fe (TDFe) decreased linearly with increasing water density along the transect, from 3.4 nmol L−1 at the two inshore stations to 1.0 nmol L−1 at the offshore stations, as a result of the vertical and lateral mixing between the Fe‐rich coastal water and the Fe‐poor Alaska Current. The Fe‐rich inshore stations were dominated by diatoms and characterized by low DMSPp : chlorophyll a (Chl a) ratios (ca. 26 nmol [g−1) and bacterial DMS yield (< 4%). In contrast, the Fe‐poor offshore stations were dominated by prymnesiophytes and exhibited high DMSPp : Chl a ratios (ca. 84 nmol [g−1) and bacterial DMS yield (8%). Chl a, DMSPp, and the abundance of total bacteria and three bacterial clades (Gammaproteobacteria, Roseobacter, and Betaproteobacteria) were positively correlated with the TDFe gradient. At the Fe‐poor offshore stations, the positive correlation found between TDFe and the DMSPp : Chl a ratios suggests that Fe supplied by mixing stimulated DMSP production in the prymnesiophyte‐dominated assemblage, a response similar to that generally observed during the first days of most of the large‐scale ocean iron fertilizations (OIFs). These results suggest that the stimulation of DMSP production takes place whatever the Fe supply mode: atmospheric dust deposition, as simulated by OIFs, or mixing, as reported in this study.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".