Organic selenium, selenate, and selenite accumulation by lake plankton and the alga <i>Chlamydomonas reinhardtii</i> at different pH and sulfate concentrations
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
Abstract Selenium (Se) concentrations measured in lake planktonic food chains (microplankton &lt;64 μm, copepods, and Chaoborus larvae) were strongly correlated with the concentrations of dissolved organic Se. These correlations were strengthened slightly by adding the concentrations of dissolved selenate to those of organic Se. To better understand the role of Se species and the influence of water chemistry on Se uptake, we exposed the green alga Chlamydomonas reinhardtii to selenite, selenate, or selenomethionine at various H+ ion and sulfate concentrations under controlled laboratory conditions. At low sulfate concentrations, inorganic Se species (selenate &gt;&gt; selenite) were more readily accumulated by this alga than was selenomethionine. However, at higher sulfate concentrations the uptake of selenite was higher than that of selenate, whereas the uptake of selenomethionine remained unchanged. Although the pH of the exposure water did not influence the uptake of selenate by this alga, the accumulation of selenomethionine and selenite increased with pH because of their relative pH-related speciation. The Se concentrations that we measured in C. reinhardtii exposed to selenomethionine were 30 times lower than those that we measured in field-collected microplankton exposed in the same laboratory conditions. This difference is explained by the taxa present in the microplankton samples. Using the present laboratory measurements of Se uptake in microplankton and of natural Se concentrations in lake water allowed us to model Se concentrations in a lake pelagic food chain. Environ Toxicol Chem 2018;37:2112–2122. © 2018 SETAC
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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 | 0.000 | 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 | 0.001 | 0.002 |
| 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 it