Methods for assessing the toxicological significance of metals in aquatic ecosystems: bio-accumulation–toxicity relationships, water concentrations and sediment spiking approaches
Bibliographic record
Abstract
Abstract Although the published literature abounds with studies showing contamination of aquatic environments by metals, there are very few data which actually demonstrate the biological impact of this contamination. Biological impacts such as alteration of in situ communities and demonstration of toxicity in environmental samples often occur at sites with elevated metal concentrations, but this does not prove that metals are actually responsible for these effects. Correlation is not proof of cause and effect. Metal-induced biological effects cannot usually be inferred from measured environmental concentrations because metal bio-availability can vary dramatically from site to site. Differences in metal bio-availability lead to differences in metal bio-accumulation, which in turn lead to differences in metal-induced effects. On the other hand, metal concentrations in biota are often much better indicators of potential biological impact than concentrations in the environment, because differences in metal bio-availability are automatically taken into account. Measurement of the body concentration of metals is a powerful tool for predicting metal effects, especially for non-essential and non-regulated metals. The body burden approach is more limited when applied to essential metals such as copper and zinc. Alternate methods which provide useful information on metal bio-availability, especially for copper and zinc, include measurement of metals in the overlying water during sediment toxicity tests, and sediment spiking with additional metal.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".