Ni Uptake by a Green Alga. 2. Validation of Equilibrium Models for Competition Effects
Bibliographic record
Abstract
It is generally admitted that the presence of major cations and H+ can attenuate trace metal uptake. Recent models such as the biotic ligand model (BLM) aim to quantify and predict this effect by determining stability constants for each of the major competitors for any given interaction of a trace metal with a biological organism. In this study, short-term Ni internalization fluxes (J(int)) were used to quantitatively assess the binding of H+, Mg2+, Ca2+ (K(H-Rs), K(Mg-Rs), K(Ca-Rs)), and trace metals to transport sites (R(s)) leading to Ni biouptake by Chlamydomonas reinhardtii. H+ and Mg2+ are shown to compete directly for the entry of Ni with affinity constants that are of the same order of magnitude (K(Mg-Rs) = 10(5.1) M(-1); K(H-Rs) = 10(5.3) M(-1)) as that measured for Ni (K(Ni-Rs) = 10(5.1) M(-1)). The Ni internalization fluxes were also strongly linked to the Mg cell status. In contrast, the role of Ca2+ could not be explained by a simple competitive equilibrium with the Ni transport sites. Aluminum (K(Al-Rs) = 10(8) M(-1)), Zn (K(Zn-Rs) = 10(6.5) M(-1)), and Cu (K(Cu-Rs) = 10(6.6) M(-1)) were all shown to compete strongly with Ni for uptake. In addition to the determination of uptake constants, these studies provide insight into the transport mechanisms of Ni by the green alga, C. reinhardtii.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".