Monitoring herd incidence of intramammary infection in lactating cows using repeated longitudinal somatic cell count measurements
Bibliographic record
Abstract
The objective of the study was to evaluate the ability of an estimate of the herd intramammary infection (IMI) incidence rate computed using repeated somatic cell count (SCC) measurements (quarter- and composite-SCC; hereafter, the SCC-derived herd IMI incidence, SCCI)to predict the incidence rate computed using repeated quarter-milk bacteriological culture (hereafter, bacteriological culture incidence, BCI) during the lactating period. A cohort of 91 Canadian dairy herds was followed in 2007 and 2008. In each herd and at each of 4 sampling periods, a series of 3 to 7 quarter-milk samples was collected from a sample of 15 cows. Routine milk bacteriological culture was conducted to identify IMI, SCC was measured on the quarter-milk samples, and composite-SCC of the preceding and following dairy herd improvement (DHI) tests were obtained. Mastitis pathogens were grouped in 3 categories: major, minor, and any pathogens. For each herd and for each period, BCI was computed for each group of organisms. Similarly, SCCI were computed using quarter- and DHI composite-SCC and using a threshold of 200,000 cells/mL to define infected quarters or cows. A linear regression model taking into account the structure of the data was used to compare the SCCI to the BCI. A similar model was used to compare fluctuations (i.e., changes from one sampling period to the next) over time of the SCCI and BCI. Measures of correlation between observed and predicted rates were computed and limits of agreement plots sketched to better explore the predictive ability of the SCCI. The quarter-milk SCC measurements that could be obtained-for instance, using on-line milking system measurements-appeared to be particularly valuable. Quarter-SCCI showed a positive and significant association with the BCI. However, limits of agreement plots indicated important disagreement for the small proportion of observations with very high BCI. Quarter-level SCCI and BCI fluctuations were also significantly associated, and a substantial correlation (Spearman rho ranging from 0.54 to 0.58) could be seen between observed and predicted rates. Conversely, the predictive value of composite-DHI SCC for monitoring IMI incidence during the lactation seemed to be quite limited. Composite SCCI was strictly associated with major IMI BCI, showed a relatively low correlation with the observed rate (Spearman rho: 0.14), and was of little help for longitudinal monitoring of the IMI incidence.
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 | 0.002 | 0.005 |
| 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 | 0.001 | 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".