Promoting effects of aluminum addition on chlorophyll biosynthesis and growth of two cultured iron‐limited marine diatoms
Bibliographic record
Abstract
Abstract Aluminum (Al) may play a role in the ocean's capacity for absorbing atmospheric CO2 via influencing carbon fixation, export, and sequestration. Aluminum fertilization, especially in iron (Fe)‐limited high‐nutrient, low‐chlorophyll ocean regions, has been proposed as a potential CO2 removal strategy to mitigate global warming. However, how Al addition would influence the solubility and bioavailability of Fe as well as the physiology of Fe‐limited phytoplankton has not yet been examined. Here, we show that Al addition (20 and 100 nM) had little influence on the Fe solubility in surface seawater and decreased the Fe bio‐uptake by 11–22% in Fe‐limited diatom Thalassiosira weissflogii in Fe‐buffered media. On the other hand, the Al addition significantly increased the rate of chlorophyll biosynthesis by 45–60% for Fe‐limited T. weissflogii and 81–102% for Fe‐limited Thalassiosira pseudonana, as well as their cell size, cellular chlorophyll content, photosynthetic quantum efficiency (Fv/Fm) and growth rate. Under Fe‐sufficient conditions, the Al addition still led to an increased growth rate, though the beneficial effects of Al addition on chlorophyll biosynthesis were no longer apparent. These results suggest that Al may facilitate chlorophyll biosynthesis and benefit the photosynthetic efficiency and growth of Fe‐limited diatoms. We speculate that Al addition may enhance intracellular Fe use efficiency for chlorophyll biosynthesis by facilitating the superoxide‐mediated intracellular reduction of Fe(III) to Fe(II). Our study provides new evidence and support for the iron–aluminum hypothesis.
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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".