Bioavailability Assessment of Metals in Freshwater Environments: A Historical Review
Bibliographic record
Abstract
Many metals (aluminum, cadmium, cobalt, copper, nickel, lead, zinc) are widely studied environmental contaminants because of their ubiquity, potential toxicity to aquatic life, and tendency for toxicity to vary widely as a function of water chemistry. The interactions between metal and water chemistry influence metal "bioavailability," an index of the rate and extent to which the metal reaches the site of toxic action. The implications of metal bioavailability for ecological risk assessment are large, with as much as a 100-fold variability across a range of water chemistries in surface waters. Beginning as early as the 1930s, considerable research effort was expended toward documenting and understanding metal bioavailability as a function of total and dissolved metal, water hardness, natural organic matter, pH, and other water characteristics. The understanding of these factors and improvements in both analytical and computational chemistry led to the development of modeling approaches intended to describe and predict the relationship between water chemistry and metal toxicity, including the free ion activity model, the gill surface interaction model, the biotic ligand model, and additional derivatives and regression models that arose from similar knowledge. The arc of these scientific advances can also be traced through the evolution of the US Environmental Protection Agency's ambient water quality criteria over the last 50 yr, from guidance in the "Green Book" (1968) to metal-specific criteria produced in the last decade. Through time, water quality criteria in many jurisdictions have incorporated increasingly sophisticated means of addressing metal bioavailability. The present review discusses the history of scientific understanding of metal bioavailability and the development and application of models to incorporate this knowledge into regulatory practice. Environ Toxicol Chem 2019;39:48-59. © 2019 SETAC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".