The effect of selenium supplementation before calving on early-lactation udder health in pastured dairy heifers
Bibliographic record
Abstract
Selenium (Se) deficiency has been associated with lowered resistance to mastitis in dairy cattle. However, little published data exists on the effect of Se supplementation before calving on udder health of pastured dairy heifers. Further, the relative efficacy of injectable barium selenate and oral organic Se for improving udder health in cows has not previously been tested. The objectives of this study were to determine the effects of precalving Se supplementation and type of supplementation on the blood activity of glutathione peroxidase and measures of udder health immediately after calving and during the first month of lactation in pastured dairy heifers. One hundred forty pregnant Chilean Holstein-Friesian heifers were fed a basal diet containing, on average, 0.15 mg of Se/kg of dry matter. One month before predicted calving, heifers were allocated to 1 of 3 groups. Group 1 (n=49) received no supplementary Se, group 2 (n=46) received a single subcutaneous injection of Se (1 mg/kg of live weight, as barium selenate), and group 3 (n=45) was fed Se yeast (3 mg/heifer/d until calving). Heifers supplemented with barium selenate had a higher glutathione peroxidase activity from 14 d in milk onwards. Selenium supplementation, irrespective of source, tended to reduce the prevalence of intramammary infection (IMI) and decrease the prevalence of quarters with high somatic cell count (SCC) at calving. Overall, Se supplementation did not result in a reduction of the incidence of new IMI or clinical mastitis or in decreased SCC during the balance of the first month of lactation. However, in pasture-based heifers injected with barium selenate before calving, and fed diets with 1.3 and 2.5 mg of Se/d precalving and during lactation, respectively, no cases of clinical mastitis were observed in the first month of lactation.
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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 | 0.001 |
| 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".