Validation of the Biotic Ligand Model in Metal Mixtures: Bioaccumulation of Lead and Copper
Bibliographic record
Abstract
The biotic ligand model (BLM) has the potential to predict biological effects and bioaccumulation in metal mixtures. Pb and Cu uptake by the green alga Chlamydomonas reinhardtii have been quantified in single-metal exposures and in metal mixtures in order to test some of the key assumptions of the BLM. Stability constants for the interaction of the metals with biological uptake sites were determined from measured short-term internalization fluxes. In the absence of competition, a value of 10(5.8) M(-1) was obtained for Cu, while 10(5.9) M(-1) was obtained for Pb. Competition experiments did not show a straightforward antagonistic competition as would be predicted by the BLM. Only at high Cu(2+) concentrations (>1 microM) did Cu behave as a competitive inhibitor of Pb transport. Surprisingly, low concentrations of Cu(2+) had a synergistic effect on Pb uptake. Furthermore, Cu uptake was independent of Pb when Cu concentrations were below 10(-7) M. In order to explain the observed discrepancies with the BLM, membrane permeability and Cu transporter expression levels were probed. The expression of ctr2, a gene coding for a Cu transporter, increased significantly in the presence of Pb, indicating that bioaccumulation is much more dynamic than assumed in the equilibrium models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".