Impact of a Coagulase-negative Staphylococci or Staphylococcus aureus Intramammary Infection during the First Month of Lactation on Somatic Cell Count, Milk Yield, and Culling in Primiparous Cows
Bibliographic record
Abstract
Mastitis in heifers is not uncommon. During the last 20 years, numerous investigations have described the nature of mastitis in heifers (Fox, 2009). Coagulase-negative staphylococci (CNS) are the most prevalent cause of intramammary infections (IMI) in heifers around calving, but Staphylococcus aureus should not be ignored as it is also prevalent, contagious, and more likely to persist into lactation. Some studies have shown a negative impact of an elevated early lactation somatic cell count (SCC) on subsequent test-day SCC, milk production and survival in heifers (De Vleigher et al, 2004-2005). The impact should vary according to the pathogen causing the IMI. The objective of this study was to determine the effect of an IMI caused by CNS or S. aureus diagnosed during the first month of lactation in heifers on SCC, milk production, and culling risk during the first lactation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".