Metal contaminants of emerging concern in aquatic systems
Bibliographic record
Abstract
Environmental context There is potential for a range of metals being used in emerging industries to pose a risk if they reach aquatic environments. This is assessed by evaluating known environmental concentrations against available toxicity data. In most instances risks are low with current usage. Areas are identified where additional data are needed. Abstract The environmental concentrations and aquatic toxicity of a range of technology-critical metals comprising platinum group and rare earth group elements, together with gallium, germanium, indium, lithium, niobium, rhenium, tantalum, tellurium and thallium, have been reviewed to determine whether they pose a risk to aquatic ecosystem health. There is a reasonable body of toxicity data for most, but the quality is quite variable, and more data are required. Chronic toxicity EC10 or NOEC values are generally in the low mg L–1 range, far higher than the current environmental concentrations in the ng L–1 range, meaning that the existing risks to ecosystem health are extremely low. Missing are reliable toxicity data for niobium and tantalum, while confounding results for lanthanum toxicity need to be resolved. There is a likelihood that the currently low concentrations of most of these elements will increase in future years. Whether these concentrations are in bioavailable forms remains to be reliably determined. For most of the elements, measured speciation information is scarce, and unfortunately the thermodynamic data required to calculate their speciation are incomplete. In addition to this problem of uncertain speciation for some of these metals, notably those present in oxidation states of III or higher, there is also a need to explore the links between speciation and bioavailability for these higher valence metals. For circumneutral solutions, the calculated concentrations of the free metal ion tend to be very low for these metals and under such conditions the link between metal speciation and bioavailability is unclear.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".