Influence of pathogens causing clinical mastitis on reproductive variables of dairy cows
Bibliographic record
Abstract
In dairy cattle, mastitis is a disease of the mammary gland caused by pathogens such as bacteria, viruses, fungi, and algae. Mastitis causes economic losses to dairy farms as well as public health concerns. The reproductive efficiency of commercial dairy herds has important implications for the economic success of dairy operations and is strongly associated with the health status of cows. Mastitis has previously been linked with decreased fertility of dairy cows, but the effect of specific pathogens on the severity of fertility reduction is still unclear. In this study, cows diagnosed with mastitis caused by major pathogens (Staphylococcus aureus, Streptococcus agalactiae, Escherichia coli, Klebsiella spp., Mycoplasma spp., and environmental Streptococcus) needed more artificial inseminations (AI) than did cows with mastitis caused by minor pathogens (coagulase-negative Staphylococcus and Corynebacterium spp.) and healthy cows. Cows diagnosed with mastitis, independent of what pathogen was causing mastitis, had more days open compared with nonmastitic cows. The percentage of cows that successfully established pregnancy at first AI was greater for the control group than for the major pathogens group but not significantly different from the minor pathogens group. Pregnancy loss was lower in the control group than in the major pathogens group; however, there was no difference compared with the minor pathogen group. Mastitis caused by gram-negative bacteria decreased the percentage of pregnancy per first AI and increased days open and pregnancy loss compared with the control group. Cows with mastitis caused by gram-positive bacteria also had increased days open compared with control cows. This study shows that different mastitis-causing bacteria can affect the fertility of cows differently. Mastitis events caused by major pathogens and gram-negative bacteria were associated with the greatest decrease in reproductive efficiency.
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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 | 0.002 |
| 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) | 0.001 | 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".