Effects of temperature on growth rate, cell composition and nitrogen metabolism in the marine diatom Thalassiosira pseudonana (Bacillariophyceae)
Bibliographic record
Abstract
Although temperature effects on phytoplankton growth and photosynthesis can be clearly demonstrated in the laboratory, their relevance in the field is much harder to establish. Recently, however, it has been recognized that temperature has a significant influence on nitrogen uptake. In particular, temperate marine diatom species may be limited by their ability to acquire nitrate at temperatures above approximately 16C. In order to explore this idea, we grew the diatom Thalassiosira pseudonana at 8, 17 and 25C, and compared cell composition, and rates of growth (), 15 N incorporation, calculated nitrate incorporation (the product of and cell N content), and the activity of nitrate reductase (NR), a key enzyme involved in nitrate incorporation. Cell N content, protein and volume remained relatively constant across different temperatures, but cell C, chlorophyll a (chl a), and C:N ratio increased with increasing temperature, suggesting that C metabolism was affected more strongly than N metabolism. Classical temperature models suggested that growth and various indices of nitrate metabolism all responded to temperature, with Q 10 values of close to 2 over the whole temperature range. However, Q 10 values over the interval from 8 to 17C were higher than 2 and much lower than 2 between 17 and 25C. Limitations to the Q 10 concept are considered. Temperature effects on different measures of nitrate metabolism were very similar, supporting the hypothesis that the effects of temperature on diatom nitrate metabolism are mediated at the level of NR activity. Recent biochemical data for NR also supports this idea.
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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".