Adverse health effects and histological changes in white sturgeon (<i>Acipenser transmontanus</i>) exposed to dietary selenomethionine
Bibliographic record
Abstract
It has been shown that selenium (Se) released to the aquatic environment can have devastating effects on local wildlife. White sturgeon (Acipenser transmontanus) have a life history particularly susceptible to contaminants, and their protection is of interest as they are culturally and economically important, and many populations are classified as endangered. During the present 72-d dietary study, multiple signs of decreased health and Se lethality were observed. Juvenile white sturgeon were given diets containing 1.4 μg, 5.6 μg, 22.4 μg, or 104.4 μg selenomethionine/g food (dry mass). Selenium accumulated in muscle and liver tissue in a dose-dependent manner. Edema causing exophthalmos developed within 15 d and 23 d, and lethal effects occurred in 54% and 22% of fish in the high- and medium-dose groups, respectively. Growth and hepatosomatic index were significantly lower in the high-dose group, which also had a high incidence of food avoidance. Histology of the liver revealed a dose-dependent increase in melanomacrophage aggregates and decrease of energy stores, which indicated toxicity. These results indicate that white sturgeon are susceptible to the effects of Se accumulation over relatively short time periods. This stresses the need for continued sturgeon research and studies looking into the environmental fate and regulation of released Se. Environ Toxicol Chem 2016;35:1741-1750. © 2015 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 | 0.001 | 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".