Iron and manganese colimitation in the Southern Ocean examined with a proteomic allocation model
Bibliographic record
Abstract
Iron (Fe) limits primary productivity in a large part of the ocean; but recent geochemical profiles and bottle incubation assays indicate that manganese (Mn) can also limit primary productivity in the Southern Ocean. Fe and Mn can interact to influence primary productivity, but the extent to which these elements colimit phytoplankton in the Southern Ocean is uncertain. In addition, current models are insufficient to assess colimitation as they assume a single, most scarce, resource. In order to examine Fe and Mn colimitation in phytoplankton, we developed a modeling framework to predict proteomic profiles under varying Fe, Mn, and light conditions. In our model, proteins are optimally allocated to various coarse-grained cellular pools, governed by environmental conditions. We predict that Fe controls cellular Mn quotas, largely because of paired stoichiometry within photosynthetic machinery. Our model suggests that the diffusion-limitation paradigm of Fe should be revisited, as diffusive flux of Fe does not appear to be limiting. We then use our model to explore various cellular mechanisms leading to phytoplankton Fe limitation. Lastly, using Fe and Mn biogeochemical model output, we predict regions in the Southern Ocean where Fe/Mn colimitation is most likely to occur.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".