Acidification increases mercury uptake by a freshwater alga, Chlamydomonas reinhardtii
Bibliographic record
Abstract
Environmental context Mercury is classified as a priority pollutant owing to the biomagnification of its methylated species along food chains and the consequent effects on top consumers. The pH of natural waters affects many of the biogeochemical processes that control mercury accumulation in aquatic organisms. Here, evidence is presented that pH affects mercury uptake by unicellular algae, primary producers in aquatic food chains, thereby providing a new example of the pervasive influence of pH on the mercury biogeochemical cycle. Abstract We have examined the influence of pH on HgII uptake (mainly in the form of the lipophilic complex HgCl2) by a green, unicellular alga, Chlamydomonas reinhardtii. Uptake of the dichloro complex increased by a factor of 1.6 to 2 when the pH was lowered from 6.5 to 5.5, an unexpected result given that the intracellular hydrolysis rate of fluorescein diacetate (FDA), used as a probe for the passive diffusion of lipophilic solutes through algal membranes, decreased in the studied alga under similar conditions. Several mechanisms were explored to explain the enhanced uptake at pH 5.5, including pH-induced changes in cell surface binding of Hg or in Hg loss rates from cells, but none of them gave completely satisfactory explanations. The present findings imply that inorganic HgII in aqueous solution behaves, in terms of uptake, neither as a lipophilic complex (the uptake of which would be expected to decrease with acidification because of algal membrane packing), nor as a cationic metal (the transport of which by facilitated transport would be expected to diminish with increasing proton concentration because of metal–proton competition at the transporter binding sites). Mercury uptake by algae seems rather to be stimulated by proton addition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.049 | 0.002 |
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; both teacher heads agree on what is shown here.
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".