Can freshwater phytoplankton access cadmium bound to low‐molecular‐weight thiols?
Bibliographic record
Abstract
Abstract Recent studies have suggested that low‐molecular‐weight (LMW) ligands can enhance trace metal uptake by marine diatoms. We have extended this work to freshwater phytoplankton and have investigated Cd bioavailability in the presence of LMW thiols, with two green algae (Chlamydomonas reinhardtii and Pseudokirchneriella subcapitata) and a cyanobacterium (Anabaena flos‐aquae). Using nitrilotriacetic acid (NTA) to hold the free Cd2+ concentration constant, we progressively added cysteine to the exposure medium and determined short‐term Cd uptake rates for each organism; the Cd uptake rates remained constant and were unaffected even when the concentrations of the Cd‐cysteine complexes exceeded that of the free Cd2+. Similar results were obtained with algae pre‐acclimated to different nitrogen sources or low zinc concentrations, even though such treatments affect their ability to utilize amino acids or take up Cd. A lack of effect was also observed for the two green algae with penicillamine, mercaptosuccinic acid, dithiothreitol and glutathione. These results suggest that the Cd‐thiol complexes do not contribute to Cd uptake. However, when the experiments were repeated with cysteine in the absence of NTA, at the same Cd2+ concentration, a significantly higher Cd uptake rate was observed in the presence of the L‐ or D‐cysteine stereoisomers than in the presence of NTA. This enhancement of Cd uptake in the presence of cysteine alone was sensitive to the nitrogen nutrition of these microorganisms, and we suggest that the enhancement likely results from biologically‐induced chemical oxidation of cysteine in the boundary layer adjacent to the algal cell.
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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) | 0.001 | 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".