Uranium complexation and uptake by a green alga in relation to chemical speciation: The importance of the free uranyl ion
Bibliographic record
Abstract
The bioavailability and toxicity of dissolved metals are closely linked to the metals' chemical speciation in solution. Normally the complexation of a metal by a ligand would be expected to decrease its bioavailability. The aqueous speciation of uranium (U) undergoes tremendous changes in the presence of ligands commonly found in natural waters (carbonate, phosphate, hydroxide, and natural organic matter). In the present project, links between speciation, medium composition, and bioavailability of uranium toward Chlamydomonas reinhardtii, a unicellular green alga, were investigated. Short-term metal uptake rates were determined in simple inorganic media at constant low pH (5.0) and hardness with particular emphasis on the differentiation between adsorbed and intracellular metal. While intracellular uptake was fairly linear over 1 h, partly reversible adsorption reached steady-state within minutes. Both adsorption and absorption were saturable processes (with a half-saturation constant Km of 0.51 microM). Addition of phosphate, citrate, or ethylenediaminetetraacetic acid (EDTA) as ligands decreased uranium bioavailability. No evidence indicating the transport of intact uranyl complexes was found (i.e., facilitated diffusion of metal bound to an assimilable ligand such as uranium-phosphate complexes). Within these experimental conditions, uranium uptake was correlated with the free uranyl ion concentration as predicted by the free-ion activity model (FIAM) and biotic ligand model (BLM).
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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 | 0.000 | 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".