Producer concern and prevalence of subclinical intramammary infections between lactations on 10 dairy goat farms in Ontario, Canada
Bibliographic record
Abstract
For many dairy goat producers, a key deciding factor for keeping does in the herd is the animal’s ability to maintain milk production. Subclinical intramammary infections (IMIs) are known to decrease milk production in does by as much as 20% (Contreras et al, Livest Prod Sci, 2003). Somatic cell count (SCC) is a reliable and inexpensive predictor of infection in dairy cows; however, this measure is highly variable in goats depending on factors such milk production, stage of lactation, and estrus activity (Leitner et al, J Dairy Sci, 2004; Paape et al, Small Rumin Res, 2007; Persson et al, Small Rumin Res, 2014), making identification of infected glands by SCC level problematic. Thus, it is likely that producers underestimate infection prevalence on their farms. While IMIs are possible throughout lactation, the highest risk period for infection is when does are transitioning from one lactation to the next. On many farms, goats are dried-off (i.e., milking is ceased) and during the dry period infections may go unchecked and new infections begin. In other situations, does are not dried-off between lactations. The aims of this study were 2-fold: 1) to assess the attitudes of the producers regarding IMIs on their farms, and 2) to determine the prevalence of these infections during the dry period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".