Metal flux through consuming interfaces in ligand mixtures: boundary conditions do not influence the lability and relative contributions of metal species
Bibliographic record
Abstract
In a mixture of metal ions and complexes, it is difficult to predict ecological risk without understanding the contribution of each metal species to biouptake. For microorganisms, the rate of uptake (internalization flux) has not only a major influence on the total metal flux but also on the bioavailability of the various metal species and their relative contributions to the total flux. In this paper, the microorganism is considered as a consuming interface, which interacts with the metal ion, M, via the Michaelis-Menten boundary conditions. The contribution of each metal complex to the overall metal flux, in relation to its lability, is examined for a number of important boundary parameters (the equilibrium constant K(a) of metal with transport sites, internalization rate constant k(int) and total transport sites concentration {R}(t)). Computations were performed for Cu(II) complexes, in a multicomponent culture medium for microoganisms. For a one-ligand system, results were acquired using rigorous mathematical expressions, whereas approximate expressions, based on the reaction layer approximation (RLA) and rigorous numerical computations (computer codes MHEDYN and FLUXY), were employed for ligand mixtures. Under the condition of ligand excess, as often found in the natural environment, the relative contribution of each metal species to the total flux is shown to be independent of the boundary conditions. This finding has important implications, including an improved basis for relating the analytical signals of dynamic metal speciation sensors to metal bioavailability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".