Usefulness of differential somatic cell count for udder health monitoring: Effect of intramammary infections, days in milk, quarter location, and parity on quarter-level differential somatic cell count and somatic cell score in apparently healthy dairy cows
Bibliographic record
Abstract
Microbial infections of the mammary gland often cause mastitis, and it can lead to substantial economic losses within the dairy industry due to its direct negative impact on milk production and composition and the associated treatment costs. Somatic cell count has emerged as a critical indicator in monitoring udder health, and recently, the large-scale availability of differential cell count analysis potentially offers new insights into underlying physiological processes. Therefore, the main objective of this study was to estimate the variation of differential SCC (DSCC) and SCC of individual quarter-level milk samples of cows according to (1) their intramammary infectious status; (2) parity of the cow; (3) quarter location; and (4) DIM at the time of sampling. A convenience sample of 5 dairy herds using an automated milking system was selected and visited every 2 wk for milk sample collection. The determination of SCC and DSCC was performed by Lactanet using a CombiFoss 7 DC instrument. The bacteriological culture was performed according to the National Mastitis Council standards. The different types of colonies (up to 10 colonies) were counted and identified using MALDI-TOF. Given the hierarchical structure of the data, a 4-level, linear mixed model with herd, cow, and quarter as random intercepts was built with either SCS or DSCC as the outcome. Differential SCC varied broadly in the SCS range 2 to 12 but tended to have a narrower variation at higher SCS levels. The effect of DIM on DSCC depended on the parity. Early in lactation, primiparous cows tended to have lower DSCC than older cows. Following 230 DIM, the DSCC in primiparous exhibited an upward trend, whereas, in older cows, it tended to decline. The quarter position did not affect either DSCC or SCS. Quarters infected with Staphylococcus chromogenes, Streptococcus dysgalactiae, Staphylococcus aureus, Staphylococcus epidermidis, "other major," and "other minor," had an increase in DSCC by ∼10.2%, 9.9%, 9.8%, 9.2%, 6.0%, and 4.9%, respectively, when compared with quarters with no growth. Our findings highlighted that IMI, parity, and DIM influenced DSCC. Therefore, these parameters should be considered when interpreting DSCC.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".