Milk selenium concentration and its association with udder health in Atlantic Canadian dairy herds
Bibliographic record
Abstract
Soils and plants in Atlantic Canadian provinces are known to contain low concentrations of selenium (Se). Earlier studies have indicated that dairy producers in Atlantic Canada are providing insufficient supplementary Se in the ration to meet the Se requirements of dairy cattle, as measured by herd-level milk Se concentration. The objective of this study was to evaluate the association between milk Se concentration and somatic cell count (SCC) and the risk of new intramammary infection (IMI) in the dry period, in Atlantic Canadian dairy cows. Eighteen dairy farms participating in the Canadian Bovine Mastitis Research Network cohort study were selected as a convenience sample. On each farm 15 cows to be dried off were selected. Quarter milk samples were collected at 4 and 2 wk before drying-off, within 24 h after calving, and at 7 d after calving to evaluate IMI status. Composite milk samples were analyzed for SCC and Se concentration. Mean milk Se concentration was marginal in 14% of the cows that were on pasture during the grazing season. Milk Se concentration was not associated with the overall odds of new IMI in the dry period; however, the odds of having a new Streptococcus spp. and other gram-positive pathogen IMI in the dry period increased with increasing milk Se concentration. Somatic cell count increased with milk Se concentration, even after adjusting for IMI status. The dairy population in our study had higher ranges for milk Se concentration, whereas ranges for prevalence of IMI, and SCC were lower, compared with those in studies where a negative relationship between Se status and udder health was first noted. Therefore, under the current management conditions, milk Se concentration did not appear to be a principal determinant of udder health.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".