Does selenium offset methylmercury toxicity across trophic levels in a primary producer, <i>Auxenochlorella pyrenoidosa</i>, and a detritovore, <i>Aeolosoma variegatum</i>?
Bibliographic record
Abstract
Methylmercury ([CH3Hg]+ or MeHg) is a potent neurotoxin that impairs functioning of the nervous system, and selenium (Se) is known to provide a protective effect against MeHg. Experiments were run to investigate the expected offsetting of MeHg toxicity by Se across trophic levels at the base of the aquatic food chain. An algal primary producer, Auxenochlorella pyrenoidosa, was exposed to MeHg (2 µg L−1) in the presence or absence of varying levels of Se (0 µg L−1, 2 µg L−1, and 5 µg L−1). Results showed that MeHg decreased the maximum density of Auxenochlorella, which Se did not offset. Equal amounts (8 mg) of the algal biomass produced under exposure conditions were subsequently fed to a detritovore, Aeolosoma variegatum (a small freshwater annelid worm). When consuming dietary Se in MeHg contaminated water, or when consuming dietary Se with dietary MeHg, Aeolosoma populations experienced a rescue effect against MeHg, i.e., they achieved greater final population density. Understanding safe levels of dietary Se that provide population level protection against MeHg is a critical step towards managing mercury in contaminated ecosystems.
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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".