Mechanistic characterization of waterborne selenite uptake in the water flea, <i>Daphnia magna</i>, indicates water chemistry affects toxicity in coal mine-impacted waters
Bibliographic record
Abstract
Abstract Concentrations of selenium that exceed regulatory guidelines have been associated with coal mining activities and have been linked to detrimental effects on aquatic ecosystems and the organisms therein. Although the major route of selenium uptake in macroinvertebrates is via the diet, the uptake of waterborne selenite (HSeO3−), the prominent form at circumneutral pH, can be an important contributor to selenium body burden and thus selenium toxicity. In the current study, radiolabelled selenite (Se75) was used to characterize the mechanism of selenite uptake in the water flea, Daphnia magna. The concentration dependence (1–32 μM) of selenite uptake was determined in 1-hour uptake assays in artificial waters that independently varied in bicarbonate, chloride, sulphate, phosphate and selenate concentrations. At concentrations representative of those found in highly contaminated waters, selenite uptake was phosphate-dependent and inhibited by foscarnet, a phosphate transport inhibitor. At higher concentrations, selenite uptake was dependent on waterborne bicarbonate concentration and inhibited by the bicarbonate transporter inhibitor DIDS (4,4′-diisothiocyano-2,2′-stilbenedisulfonic acid). These findings suggest that concentrations of phosphate in coal mining-affected waters could alter selenite uptake in aquatic organisms and could ultimately affect the toxic impacts of selenium in such waters.
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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) | 0.000 | 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".