Usefulness of differential somatic cell count for udder health monitoring: Association of differential somatic cell count and somatic cell score with quarter-level milk yield and milk components
Bibliographic record
Abstract
Mastitis is the most common disease affecting dairy cattle and is associated with substantial milk loss. Somatic cell count has been widely used as an indicator of udder inflammation (e.g., subclinical mastitis). More recently, differential somatic cell count (DSCC) has become available as an auxiliary tool for milk quality control, with the potential to indicate different stages of inflammation when combined with SCC. This paper aimed to investigate the association of SCS and DSCC with milk yield and milk components at the quarter level. A convenience sample of 5 dairy herds using an automated milking system (AMS) was selected and visited every other week for milk sample collection. Fat, protein, and lactose content were analyzed at Lactanet (Canadian Network for Dairy Excellence, Sainte-Anne-de-Bellevue, Quebec, Canada) by mid-infrared spectroscopy using a MilkoScan FT6000. Determination of SCC and DSCC was also performed by Lactanet using a CombiFoss 7 DC instrument. Milk yield data were retrieved from the AMS. Given the hierarchical structure of the data, a linear mixed model was built with either milk yield or milk components as the outcomes. The results showed that elevated SCS, in combination with lower proportions of DSCC, was associated with the highest milk loss for primiparous and multiparous cows. For instance, the estimated milk loss for a quarter with a SCS of 7 and a DSCC of 55% was 1.45 kg/d compared with a quarter with a SCS of 2 and DSCC of 65% in multiparous cows. The association was similar when the outcome was the lactose content. Quarters with elevated SCS and lower DSCC had the lowest lactose percentage. No notable changes in fat content were observed across different SCS levels in multiparous cows, and quarters with higher DSCC had the lowest fat percentage in primiparous and multiparous cows. Protein content tended to be lower in quarters with increased SCS and low DSCC. In quarters from primiparous cows with DSCC levels above 70%, protein content showed slight variation across SCS levels. For multiparous cows, however, protein content remained relatively stable across different SCS and DSCC levels. In conclusion, our findings revealed that the combination of elevated SCS and low DSCC was associated with the most substantial milk loss. These results could be used to optimize udder health management.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".