Metal-phytoplankton interactions: Modeling the effect of competing ions (H+, Ca2+, and Mg2+) on uranium uptake
Bibliographic record
Abstract
The influence of pH and hardness cation concentrations on uranium uptake by a green alga, Chlamydomonas reinhardtii, was investigated through short-term exposure experiments. Uranium uptake at pH 5 and at pH 7 was measured over a large concentration range (0.020-2.0 microM 233U), and the effects of hardness cations were studied over environmentally pertinent concentration ranges (approximately 0.05-2 mM) at a constant uranium concentration (0.25 microM). Calcium and magnesium inhibited uranyl uptake, but the influence of pH was more complex than anticipated. The equilibrium biotic ligand paradigm of metal bioavailability predicts that two distinct phenomena of antipathetic effect will influence uranium availability as pH is varied. Increasing pH reduces the concentration of protons, thus reducing competition for the physiologically active sites, whereas the concomitant complexation by carbonates and hydroxides reduces the free uranyl activity. Maximum uranium uptake rates observed at pH 7, however, were far greater than those observed at pH 5, suggesting a noncompetitive inhibition of metal transport by protons. Modeling on the basis of our results strongly suggests that cells grown and exposed at pH 7 have either a greater internalization rate of uranyl or a higher number of transport sites compared with cells grown and exposed at pH 5. We thus conclude that the simple proton-metal competition described by the biotic ligand model cannot successfully depict uranium-algae interactions. The development of an appropriate model incorporating the influence of protons to predict metal uptake and toxicity will be more challenging than anticipated.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".