Bioavailability Assessment of Metals in Freshwater Environments: A Historical Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 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 it