When are metal complexes bioavailable?
Bibliographic record
Abstract
Environmental context The concentration of a free metal cation has proved to be a useful predictor of metal bioaccumulation and toxicity, as represented by the free ion activity and biotic ligand models. However, under certain circumstances, metal complexes have been shown to contribute to metal bioavailability. In the current mini-review, we summarise the studies where the classic models fail and organise them into categories based on the different uptake pathways and kinetic processes. Our goal is to define the limits within which currently used models such as the biotic ligand model (BLM) can be applied with confidence, and to identify how these models might be expanded. Abstract Numerous data from studies over the past 30 years have shown that metal uptake and toxicity are often best predicted by the concentrations of free metal cations, which has led to the development of the largely successful free-ion activity model (FIAM) and biotic ligand model (BLM). Nonetheless, some exceptions to these classical models, showing enhanced metal bioavailability in the presence of metal complexes, have also been documented, although it is not yet fully understood to what extent these exceptions can or should be generalised. Only a few studies have specifically measured the bioaccumulation or toxicity of metal complexes while carefully measuring or controlling metal speciation. Fewer still have verified the fundamental assumptions of the classical models, especially when dealing with metal complexes. In the current paper, we have summarised the exceptions to classical models and categorised them into five groups based on the fundamental uptake pathways and kinetic processes. Our aim is to summarise the mechanisms involved in the interaction of metal complexes with organisms and to improve the predictive capability of the classic models when dealing with complexes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".