Interactive effects of iron and temperature on the growth of <scp><i>Fragilariopsis cylindrus</i></scp>
Bibliographic record
Abstract
Abstract Iron and temperature are important drivers controlling phytoplankton growth in the Southern Ocean (SO). Most studies examining phytoplankton responses to these variables consider them independently, testing responses to changing temperature under constant iron and vice versa. Consequently, we lack a phenomenological and mechanistic understanding of how concurrent changes in these variables influence primary productivity. Here, we used a matrix of three temperatures and eight iron levels to examine changes in growth rate, photophysiology, and size in Fragilariopsis cylindrus . Temperature and iron interactively influenced growth; warming decreased iron demand, allowing cells to maintain half‐maximal growth rate at lower iron concentrations. We also observed possible mechanisms underpinning this phenomenon: warming increased light‐harvesting cross section and reduced cell size, thereby increasing light energy availability and iron uptake efficiency. These results suggest that interactive iron‐warming effects could lead to larger increases in SO phytoplankton growth than those currently predicted by marine ecosystem models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".