Aluminum increases net carbon fixation by marine diatoms and decreases their decomposition: Evidence for the iron–aluminum hypothesis
Bibliographic record
Abstract
Abstract Recent studies indicate that aluminum (Al) could play an important role in the ocean carbon cycle by increasing phytoplankton carbon fixation and reducing organic carbon decomposition. However, how Al may influence the decomposition of organic carbon has not yet been explicitly examined. Here we report the effects of Al on carbon fixation by marine diatoms and their subsequent decomposition. By using radiocarbon as a tracer, the carbon fixation and decomposition of three model marine diatoms were examined in Aquil* media at different concentrations (0, 40, 200, and 2000 nM) of dissolved Al. Addition of Al enhanced net carbon fixation by the diatoms in the declining growth phase (by 9%–29% for Thalassiosira pseudonana, 15%–20% for T. oceanica, 15%–23% for T. weissflogii). Under axenic conditions the decomposition rates (d−1) of the diatom‐produced particulate organic carbon (POC) significantly decreased (by 21%–57% for T. pseudonana, 0%–41% for T. oceanica, 29%–58% for T. weissflogii) in the Al‐enriched treatments. In the presence of bacteria, the decomposition rates of T. weissflogii‐produced POC were still 37%–38% lower in Al‐enriched treatments compared to the control. Significant increases in cell size, cellular carbon content (pmol C/cell) and cellular carbon density (pmol C/μm3) of T. weissflogii were also observed in the Al‐enriched treatments compared to the control. The Al‐related increase in net carbon fixation and cell size, and the decrease in POC decomposition rate may facilitate carbon export to ocean depths. The study provides new evidence for the iron–aluminum hypothesis, which suggests that Al could increase phytoplankton uptake of atmospheric CO2 and influence climate change.
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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 | 0.001 | 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".