Organic selenium, selenate, and selenite accumulation by lake plankton and the alga <i>Chlamydomonas reinhardtii</i> at different pH and sulfate concentrations
Bibliographic record
Abstract
ion and sulfate concentrations under controlled laboratory conditions. At low sulfate concentrations, inorganic Se species (selenate >> 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".