Chemical Conditions in the Boundary Layer Surrounding Phytoplankton Cells Modify Cadmium Bioavailability
Bibliographic record
Abstract
In this study we tested the hypothesis that metal uptake by unicellular algae may be affected by changes in metal speciation in the boundary layer surrounding the algal cells. The freshwater alga Chlamydomonas reinhardtii was preacclimated to different N nutrition regimes; changes in N nutrition are known to change the nature of extracellular metabolites (e.g., reactive oxygen species “ROS”, and OH – ) and thus boundary layer chemical conditions. Specifically, at a constant bulk free Cd 2+ concentration, Cd uptake by N-starved algae in cysteine-buffered solution was significantly higher than that in NTA-buffered solution. This enhancement was likely due to an increase of the free Cd 2+ concentration in the boundary layer, resulting from localized cysteine oxidation by ROS released from these algae. On the other hand, Cd uptake was markedly lower when the free Cd 2+ concentration near cell surface decreased as a result of an increase in the boundary layer pH of nitrate-acclimated algae or enhanced localized metal complexation. The results imply that redox, acid–base and metal complexation processes in the boundary layer differ from those in bulk water, even under chemically stable bulk conditions, and the boundary layer effect may well be of significance to phytoplankton acquisition of other trace metals.
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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 | 0.001 | 0.001 |
| 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".