Metal bioavailability in aquatic systems— beyond complexation and competition
Bibliographic record
Abstract
Metal bioavailability in solution is mostly driven by two factors: complexation and competition. The first factor, complexation, contributes to decrease the overall reactivity of the metal by reducing the activity of the free metal ion, which is known as the common denominator of metal reactions involving either dissolved ligands or surface functional groups (abiotic or biotic). Ubiquitous in natural ecosystems, natural organic matter is, for several metals, the most important metal complexing ligand. The second factor, competition, contributes to decrease the availability of biotic ligands involved in the membrane transport of metals from the bulk solution to the intracellular medium. In freshwater systems, proton and hardness cation concentrations are the main parameters potentially modulating metal bioavailability. The above reflects the current accepted paradigm. In this paper, two knowledge gaps are identified: i) the role of natural organic matter other than metal complexation that may lead to an increase in metal bioavailability; and ii) the effects of multiple metals other than competition that may trigger biological feedback mechanisms which may, in turn, alter biotic ligand binding properties. More research efforts are needed to decipher the extent of these overlooked potential effects and to improve the predictability of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".