In the Presence of Fluoride, Free Sc<sup>3+</sup>Is Not a Good Predictor of Sc Bioaccumulation by Two Unicellular Algae: Possible Role of Fluoro-Complexes
Bibliographic record
Abstract
We investigated the effect of fluoride complexation on scandium accumulation by two unicellular algae, Chlamydomonas reinhardtii and Pseudokirchneriella subcapitata. This trivalent metal was selected for its chemical similarities with aluminum and for its convenient radioisotope (Sc-46), which can be used as a tracer in short-term bioaccumulation studies. Scandium surface-bound concentrations (Sc(ads)) and uptake fluxes (J(int)) were estimated in the two algae over short-term (<1 h) exposures at pH 5 and in the presence of 0 to 40 μM F(-). Although the computed proportion of dissolved Sc(3+) dropped from 20% to 0.01% over this [F(-)] range, Sc(ads) and J(int) values for both algae decreased only slightly, suggesting a participation of Sc fluoro-complexes in both processes. Surface adsorption and uptake of fluoride complexes with aluminum have been reported in the literature. These observations are not taken into account by current models for trace metal bioaccumulation (e.g., the biotic ligand model). Results from a previous study, where the effects of pH on Sc uptake were investigated, suggested that Sc hydroxo-complexes were internalized by C. reinhardtii. There is thus growing evidence that the free ion concentration may not be adequate to predict the accumulation of Sc (and potentially of other trivalent metals) in aquatic organisms.
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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) | 0.000 | 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 | 0.001 | 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".