Influence of chloride on silver uptake by two green algae, <i>Pseudokirchneriella subcapitata</i> and <i>Chlorella pyrenoidosa</i>
Bibliographic record
Abstract
Silver bioavailability in the presence of chloride was estimated from short-term (< or = 60 min) uptake experiments with two green algae, Pseudokirchneriella subcapitata and Chlorella pyrenoidosa. In the first experiment, silver uptake was monitored under two concentration regimes in which total dissolved silver ([Ag]tot) and [Cl] were manipulated to maintain [Ag+] at a constant value (10 nM). Comparable uptake rates were measured for both treatments despite the dramatic changes in [Cl] and [Ag]tot. In the second experiment, ambient [Ag]tot was held constant (10 or 115 nM), but [Cl] was varied (0.005-50 mM) to explore the whole range of silver chloro-complexes. Intracellular silver varied markedly along the [Cl] gradient and exhibited a clear, positive correlation with ambient [Ag+] for both algae. We conclude that the biotic ligand model reliably describes silver bioavailability in the presence of chloride for the two test algae and that its applicability depends on the relative magnitudes of silver fluxes through the unstirred diffusion layer and across the cell membrane, with the latter being affected by the presence or absence of a Cu(I) transporter. In the presence of chloride, no evidence was found for the internalization of silver via anion transport or passive diffusion of the neutral mono-chloro-complex, AgCl0.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".