Microalgal‐driven pH changes in the boundary layer lead to apparent increases in Pb internalization by a unicellular alga in the presence of citrate
Bibliographic record
Abstract
Abstract Several cases have been described in the literature where different ligands enhanced lead (Pb) bioavailability over what would have been expected on the basis of equilibrium models such as the biotic ligand model (BLM). These exceptions compromise the development of BLMs for this metal, and mechanistic knowledge of the involved processes is still insufficient. The present study shows that the hydrophilic organic ligand citrate enhances Pb internalization by Chlamydomonas reinhardtii for both the wild and the wall‐less strains. Despite the high Pb internalization fluxes shown by this alga, which may be near the limits set by diffusion through the boundary layer, and its capacity to assimilate citrate (and potentially Pb‐citrate complexes), neither of these mechanisms could quantitatively account for the observed increase in Pb internalization in the presence of this ligand. However, algal‐driven pH increases in the boundary layer and concomitant changes in Pb speciation successfully explained the observed results. This study suggests that information on bulk solution chemistry is not enough to predict metal bioavailability for organisms that can substantially modify the chemical composition of their boundary layer, as observed for C. reinhardtii.
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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) | 0.001 | 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".