Higher temperature, increased <scp>CO<sub>2</sub></scp>, and changing nutrient ratios alter the carbon metabolism and induce oxidative stress in a cosmopolitan diatom
Bibliographic record
Abstract
Abstract Phytoplankton are responsible for about 90% of the oceanic primary production, largely supporting marine food webs, and actively contributing to the biogeochemical cycling of carbon. Yet, increasing temperature and p CO 2 , along with higher dissolved nitrogen: phosphorus ratios in coastal waters are likely to impact phytoplankton physiology, especially in terms of photosynthetic rate, respiration, and dissolved organic carbon (DOC) production. Here, we conducted a full‐factorial experiment to identify the individual and combined effects of temperature, p CO 2 , and N : P ratio on the antioxidant capacity and carbon metabolism of the diatom Phaeodactylum tricornutum . Our results demonstrate that, among these three drivers, temperature is the most influential factor on the physiology of this species, with warming causing oxidative stress and lower activity of antioxidant enzymes. Furthermore, the photosynthetic rate was higher under warmer conditions and higher p CO 2 , and, together with a lower dark respiration rate and higher DOC exudation, generated cells with lower carbon content. An enhanced oceanic CO 2 uptake and an overall stimulated microbial loop benefiting from higher DOC exudation are potential longer‐term consequences of rising temperatures, elevated p CO 2 as well as shifted dissolved N : P ratios.
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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 | 0.001 |
| 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".