Barium selenate supplementation and its effect on intramammary infection in pasture-based dairy cows
Bibliographic record
Abstract
A significant proportion of cattle receive inadequate dietary Se because of its low content in soils and pastures of various regions of the world. Several economically important diseases in dairy cows, such as mastitis, have been associated with Se deficiency. The objective of this study was to evaluate the effect of a single injection of a long-acting form of Se at drying off on the risk and incidence rate of new intramammary infections and on milk somatic cell count in the subsequent lactation in pasture-based dairy cows. Forty-nine Chilean Holstein-Friesian cows were fed a diet containing <0.05 mg of Se/kg of ration dry matter. During the dry period, cows were allocated to 1 of 2 groups, a supplemented (n=24) group treated with a single subcutaneous injection of barium selenate 2 mo before calving and a control group (n=25) that remained unsupplemented. Duplicate foremilk samples were aseptically collected within 6 d after calving and every 2 wk until drying-off for bacteriological culture. Milk samples were also collected monthly for somatic cell count evaluation. Blood samples were collected before treatment and at 30, 90, 180, and 270 d after treatment for analysis of blood glutathione peroxidase (GPx) activity. The activity of glutathione peroxidase was higher in supplemented cows 30 d after the injection until the end of the study. The risk and incidence rate of new intramammary infections was not affected by supplementation. A progressive increase in somatic cell count was observed throughout lactation, but there was no effect of supplementation. In conclusion, a one-time injection of barium selenate 2 mo before calving in these pasture-based dairy cows did not affect udder health in the subsequent lactation, indicating that Se basal intake was adequate for preventing subclinical mastitis in pasture-based cows in southern Chile.
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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) | 0.000 | 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".