The Biotic Ligand Model Can Successfully Predict the Uptake of a Trivalent Ion by a Unicellular Alga Below pH 6.50 but not Above: Possible Role of Hydroxo-Species
Bibliographic record
Abstract
In many reported cases, the biotic ligand model (BLM) has been shown to predict the bioavailability of divalent metals toward aquatic biota successfully. However, studies on the bioavailability of nonessential trivalent metals, including aluminum (Al), are relatively scarce. In the present study, short-term scandium (Sc) internalization fluxes (Jint) were measured in Chlamydomonas reinhardtii in order to explore the applicability of the BLM to trivalent metals. Scandium was selected for its chemical similarities with Al and for its convenient radio-isotope (Sc-46). Apparent affinity constants of Sc(3+) with membrane transport sites (KSc-Rcell app) were surprisingly high, ranging from 10(8.08) M(-1) to 10(13.95) M(-1) over the pH range from 4.50 to 7.90. The competition of H(+) for binding with Sc(3+) transport sites explained this trend within the pH range of 4.50 to 6.00, but not from pH 6.50-7.90. In this latter pH range, predicted fluxes were smaller than observed fluxes and this divergence increased with pH, from a factor of 4 to approximately 1000. Above pH 6.50, the calculated supply of Sc(3+) to the biointerface by physical diffusion of the free Sc(3+) ion and by the dissociation of its hydroxo-complexes (ScOH(2+), Sc(OH)2(+) and Sc(OH)3) was insufficient to support the high observed internalization fluxes. We speculate that this failure of the BLM could be due to the transmembrane transport of undissociated Sc hydroxo-complexes. Scandium uptake could be modeled reasonably well using a simple semiempirical equation considering equal contributions from Sc(3+), ScOH(2+), Sc(OH)2(+), and Sc(OH)3 and no H(+) competition. Our work highlights the importance of studying the possible role of hydroxo-species in trace metal uptake.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".