36 Impact of feeding different selenium (Se) sources to pregnant and lactating ewes on lamb Se enrichment and serum biochemistry profiles
Bibliographic record
Abstract
Abstract Abstract: Introduction Global pre-weaning lamb mortalities have remained above 10%, a detriment to farm profits. Selenium (Se) is a trace mineral vital to several functions in sheep. Se supplementation during late pregnancy and lactation, particularly organic Se supplementation, may produce more robust lambs. Objective To determine how differences in maternal Se supplementation impact lamb health within 10 days postpartum (ppd 10). Methods Ewes (n = 110) were enrolled in a feeding trial from gestation day (gd) 110 to ppd 10, and supplemented with either no Se, 0.3 mg/day inorganic Se, or 0.3 or 0.6 mg/day organic Se. Lambs only received Se via maternal nursing. Lamb serum was collected on ppd 0, 2, and 10 to assess maternal transfer of Se, and muscle samples were collected on ppd 10 to assess lamb Se stores. Serum glutathione peroxidase (GPx) levels, thyroid hormone triiodothyronine (T3) levels, and a complete ovine 23-parameter biochemistry panel were assessed on ppds 0 and 10. Results Maternal organic Se supplementation significantly increased Se levels in lamb serum (P < 0.0001) and muscle (P < 0.0001) as compared to other treatments. Organic Se supplementation significantly increased GPx activity at birth (P > 0.001), but T3 levels were not affected. From the biochemical panel, serum levels of non-esterified fatty acid, potassium, chloride, albumin, total bilirubin, creatine kinase, aspartate aminotransferase, and glutamate dehydrogenase showed significant treatment differences. Conclusions Organic Se supplementation to ewes during late gestation and lactation improved lamb Se status and antioxidant capacity at birth. Other serum biochemistry parameters were only marginally affected by Se treatment. (Supported by NSERC 401814)
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) | 0.001 | 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".