Extending the Biotic Ligand Model to Account for Positive and Negative Feedback Interactions between Cadmium and Zinc in a Freshwater Alga
Bibliographic record
Abstract
Low concentrations of essential trace metals such as zinc (Zn) were recently shown to strongly modulate cadmium (Cd) uptake in the freshwater alga Chlamydomonas reinhardtii. Here we studied the mechanisms of Cd and Zn acquisition by this alga, using metal uptake kinetics experiments. Cadmium uptake rates fitted a three transport site model characterized by the affinity constants K(Cd–1)(Cd) = 10(5.0), K(Cd–2)(Cd) = 10(7.6), and K(Cd–3)(Cd) = 10(8.8). Similar uptake kinetics were obtained for Zn with K(Zn–1)(Zn) = 10(5.0), K(Zn–2)(Zn) = 10(7.4), and K(Zn–3)(Zn) > 10(9). Competitive binding experiments suggest that Zn and Cd share the same three transport systems. The capacities of the transport systems were modulated by as much as 10-fold following preacclimation to high or low Zn(2+) and Cd(2+) concentrations. We conclude that the strong protective effect of Zn on Cd accumulation is mainly due to the reduction of the maximal uptake rate of the high-affinity Zn–2 (or Cd–2) transport system. A biotic ligand model was developed to incorporate the effects of both chemical speciation and physiological regulation of Cd transport systems. The model successfully predicts the experimentally measured steady-state Cd content of C. reinhardtii in the presence of low or high [Zn(2+)].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".