The Effect of Major Ions and Dissolved Organic Matter on Complexation and Toxicity of Dissolved Thallium to <i>Daphnia magna</i>
Bibliographic record
Abstract
Abstract Thallium (Tl) is a trace element associated with base metal mining and processing, but little is known regarding how its toxicity is influenced by water chemistry. In the present study, the 48-h median lethal concentration (LC50) of Tl to Daphnia magna was determined in a standard laboratory water, and toxicity was reassessed under conditions of varying cation (Ca2+, K+, Na+), anion (Cl–, HCO–3), and dissolved organic matter (DOM) concentrations. The calculated 48-h LC50 of 1.86 mg Tl/L was consistent with previous work on Tl toxicity to D. magna. At the 48-h LC50 concentration, changes in water chemistry had no statistically significant effect on mortality, although there was a trend toward lower Tl toxicity with elevated water K+. Test waters containing 10 mM CaCl2 did not support control survival. The measurement of Tl complexation with DOM using asymmetric flow field flow fractionation confirmed the outcomes of biogeochemical speciation modeling: Tl speciation was relatively unaffected by water chemistry, and the majority of Tl remained in the ionic form across all treatments. These data indicate that Tl toxicity is largely independent of speciation, a property that will greatly simplify risk assessments for this metal in freshwaters. Environ Toxicol Chem 2019;38:2472–2479. © 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".