Interactive effects of temperature and nitrogen source on the elemental stoichiometry of a polar diatom
Bibliographic record
Abstract
Abstract A recent study hypothesized that the near‐zero temperatures that generally prevail in Arctic waters negate the influence that different nitrogen (N) sources can otherwise have on the growth and elemental stoichiometry of marine micro‐algae. Here we test this hypothesis experimentally by evaluating how temperature (0–9°C) affects the growth and elemental stoichiometry of an ecologically relevant Arctic diatom Chaetoceros gelidus growing on different N sources (ammonium, nitrate, urea) at saturating irradiance. Following an initial acclimation period in which steady growth rates were achieved under each experimental treatment, changes in cellular concentrations of chlorophyll a and particulate carbon (C), nitrogen (N), phosphorus (P), and biogenic silica (Si) were monitored. While N source effects on growth rate became manifest as temperature rose above 0°C, the estimated optimal growth temperature was similar in all cases (Topt = 8.3°C). A positive effect of temperature on the N : P ratio occurred only at 6°C. Above this temperature, the N : P ratio decreased to values close to those observed at 0°C and 3°C. By contrast, the C : N ratio remained nearly invariant between 0°C and 6°C but increased substantially at 9°C. Overall, the results suggest that the presently widespread and successful diatom C. gelidus possesses the ability to remain competitive despite ongoing environmental changes and that its responses to warming and the availability of different N sources may impact elemental fluxes in the future.
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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) | 0.001 | 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".