Effects of Maternally Transferred Egg Selenium on Embryo-Larval Survival, Growth, and Development in Arctic Grayling (<i>Thymallus arcticus</i>)
Bibliographic record
Abstract
Abstract Selenium (Se) toxicity to fish is primarily manifested via maternal transfer to the eggs, which may result in adverse effects on larval survival and development. The present study assessed the effects of egg Se concentrations derived via maternal transfer on early life–stage development, survival, and growth of Arctic grayling (Thymallus arcticus), a salmonid species not previously assessed for Se sensitivity. Fish gametes were collected from 4 streams in Alaska known to exhibit a range of egg Se concentrations. Eggs were fertilized and reared in the laboratory from hatch through post-swim-up. Larvae were assessed for survival, length, and weight, as well as deformities (skeletal, craniofacial, fin-fold) and edema based on a graduated severity index. Eggs from a total of 47 females were collected, with egg Se concentrations ranging from 3.3 to 33.9 mg kg−1 dry weight. No relationships were observed between larval endpoints evaluated and parent females' egg, muscle, or whole-body Se concentrations. Therefore, Se 10% effective concentrations (EC10s) were defined as the maximum measured Se concentrations: >33.9, >17.6, and >19.7 mg kg−1 dry weight for eggs, muscle, and whole-body tissue, respectively. Collectively, these data indicate that Arctic grayling are relatively insensitive to maternally transferred Se compared to other fish species. Environ Toxicol Chem 2021;40:380–389. © 2020 SETAC
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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".