Relationship between Leukocyte Population and Nutritive Conditions in Dairy Herds with Frequently Appearing Mastitis
Bibliographic record
Abstract
To clarify the relationship between cellular immune status and nutritive condition, feeding program, blood profiles, and leukocyte populations were analyzed in two dairy herds experiencing frequent mastitis. Fourteen of the 35 lactating cows in herd A, and 18 of the 50 lactating cows in herd B scored positive on the California Mastitis Test (CMT), and 3 of the 73 lactating cows were CMT positive in herd C, which was the control. All herds were evaluated during five different milking stages, and blood was collected from five cows at each stage. With regard to feed content, the percentages of total digestible nutrients (TDN) and crude protein (CP) were found to be lower in herds A and B than in herd C. Levels of serum total cholesterol and blood urea nitrogen were lower in herds A and B than those in herd C. Neutrophil counts in herds A and B were increased compared to the neutrophil counts in herd C. On the other hand, the numbers of CD3(+) T cells and CD14-MHC class(+) cells were lower in herd A and B than in herd C. A decrease in peripheral lymphocytes and undernourishment were observed in the herds with frequent occurring mastitis.
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 | 0.001 |
| 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".