Why Does Cysteine Enhance Metal Uptake by Phytoplankton in Seawater but Not in Freshwater?
Bibliographic record
Abstract
Low-molecular-weight weak ligands such as cysteine have been shown to enhance metal uptake by marine phytoplankton in the presence of strong ligands, but the effect is not observed in freshwater. We hypothesized that these contrasting results might be caused by local cysteine degradation and a Ca effect on metal–ligand exchange kinetics in the boundary layer surrounding the algal cells; newly liberated free metal ions cannot be immediately complexed in seawater by Ca-bound strong ligands but can be rapidly complexed by free ligands at low-Ca levels. The present results consistently support this hypothesis. At constant bulk Cd 2+ concentrations, buffered by strong ligands: (1) at 50 mM Ca, cysteine addition significantly enhanced Cd uptake in high-Ca preacclimated euryhaline Chlamydomonas reinhardtii (cultured with cysteine as a nitrogen source to enhance local Cd 2+ liberation via cysteine degradation); (2) at 0.07 mM Ca, this enhancement was not observed in the algae; (3) at 50 mM Ca, the enhancement disappeared when C. reinhardtii were cultured with ammonium (to inhibit cysteine degradation and local Cd 2+ liberation); (4) cysteine addition did not enhance Cd uptake by cysteine-cultured marine Thalassiosira weissflogii when the concentration of immediately reacting strong ligands was sufficient to complex local Cd 2+ liberation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".