Copper and lead internalisation by freshwater microalgae at different carbonate concentrations
Bibliographic record
Abstract
Environmental context Metal–carbonato complexes have been reported to contribute to metal uptake and toxicity in aquatic organisms. We show that in the presence of lead–carbonato complexes, Pb internalisation by the microalga Chlamydomonas reinhardtii is higher than that predicted on the basis of the free Pb2+ concentration. This effect, which was not observed for another microalga that takes up Pb more slowly, is attributed to the very high rates of Pb uptake by C. reinhardtii, which result in diffusion limitation. Abstract The possible contribution of metal–carbonato complexes to metal uptake or toxicity has been mentioned several times in the literature, often in studies where dissolved inorganic carbon (DIC) concentrations and pH were varied together, but a thorough study of the effect on DIC on metal bioavailability to aquatic organisms has not been done. By using closed systems – allowing changes in DIC concentrations at fixed pH – and ion selective electrodes to determine free metal ion concentrations, we show that lead internalisation by the unicellular alga Chlamydomonas reinhardtii in the presence of high DIC concentrations is higher than predicted by the free Pb2+ ion concentration at bulk [Pb2+] lower than 50 nM, but not at higher [Pb2+]. This effect is not observed for another microalga, Chlorella vulgaris, which shows a lower rate of Pb internalisation. Copper internalisation by C. reinhardtii seems also to be slightly higher than predicted on the basis of free Cu2+ at low (20 nM) bulk Cu2+ concentrations but not at higher ones. The possibility that Pb (and Cu) internalisation by C. reinhardtii is partially limited by diffusion from the bulk solution to the algal surface is identified and discussed as a possible explanation for these results.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".