Monitoring dry period intramammary infection incidence and elimination rates using somatic cell count measurements
Bibliographic record
Abstract
The objective of the study was to evaluate the predictive ability of the herd dry period (DP) intramammary infection (IMI) incidence and elimination rates derived from predry and postcalving somatic cell count (SCC) measurements [quarter-level SCC and dairy herd improvement (DHI) composite-level SCC] for monitoring the herd DP IMI incidence and elimination rates. A cohort of 91 Canadian dairy herds was followed from 2007 to 2008. In each herd, a sample of 15 cows was selected each year, and a series of 2 predry and 2 postcalving quarter milk samples were collected. Routine milk bacteriological culture was conducted to identify IMI, SCC was measured on the quarter milk samples, and composite SCC of the last predry and first postcalving DHI tests were obtained. Mastitis pathogens were grouped into 3 categories: major pathogens, minor pathogens, and any pathogens. For each herd, DP bacteriological culture-derived IMI incidence and elimination rates were computed using quarter milk culture data. Similarly, SCC-derived herd incidence and elimination rates were computed using quarter and DHI composite-level SCC measurements and using various SCC thresholds to define new and eliminated IMI. Linear regression was used to compare herd quarter-level and composite-level SCC-derived herd incidence and elimination with DP bacteriological culture-derived IMI incidence and elimination. Herd DP incidences computed by using quarter-level SCC, and with most of the SCC thresholds tested, were significant predictors of the DP major, minor, and any IMI incidences (F-test; P≤0.05). The highest coefficients of determination (R(2)) were obtained with thresholds of 200,000 (R(2): 12%) and 50,000 cells/mL (R(2): 25%) for predicting major and minor IMI, respectively. When using composite DHI SCC measurements, however, substantial losses of predictive power were seen for minor and any IMI incidences compared with quarter-level SCC. For DP major IMI incidence, composite SCC yielded similar, but modest, predictive power when a cutoff value of 150,000 cells/mL was chosen to define new IMI. To predict DP elimination rates, the value of quarter-level SCC seemed limited to predicting the DP major IMI elimination rate. Composite SCC, on the other hand, showed modest predictive power for major and minor IMI elimination rates, with thresholds of 200,000 and 50,000 cells/mL, respectively. Results from the current study suggest that quarter and composite SCC-derived rates could be used as substitutes for bacteriological culture-derived rates for some groups of mastitis pathogens.
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".