Influence of Humic Acid on Algal Uptake and Toxicity of Ionic Silver
Bibliographic record
Abstract
The biogeochemical cycle of silver has been profoundly disturbed by various anthropogenic activities. To better understand the relationship among silver speciation, bioavailability, and toxicity in freshwaters, we have studied the short-term uptake of silver by two species of green algae, Chlamydomonas reinhardtii and Pseudokirchneriella subcapitata, in the presence or absence of a well-characterized humic acid (Suwannee River Humic Acid, SRHA). The free Ag(+) concentrations in the exposure solutions were determined using an equilibrium ion-exchange technique. According to the biotic ligand model, for a given free metal ion concentration, metal uptake should remain the same in the presence or absence of humic acid. However, short-term silver uptake in the presence of SRHA was greater than anticipated on the basis of free Ag(+) concentration. Subsequent determination of silver subcellular distribution revealed that significantly more silver was present in the "cell debris" fraction (known to contain the cell wall and fragmented membranes) in the presence of SRHA than in its absence. Finally, this increase in silver uptake in the presence of humic acid did not result in decreased algal growth. These results suggest that the increase in silver uptake observed in the presence of SRHA is surface-bound, not truly internalized.
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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".