325 The Diagnosis of Sub-acute Ruminal Acidosis (SARA) in Dairy Cows on Commercial Farms Using the Milk Fatty Acid Profile
Bibliographic record
Abstract
Abstract The accuracy of the milk fatty acid profile as a diagnostic tool for the diagnosis of sub-acute ruminal acidosis (SARA) has been determined when SARA was experimentally induced. This had not yet been done not on commercial dairy farms, where SARA can occur naturally. The objective of this study was to determine this accuracy in individual cows on commercial dairy farms. A total of 336 cows from 24 commercial dairy farms in Quebec were included. Farms were blocked based on geographical location and management, with each block having one high risk SARA farm and one low risk SARA farm. Farm Risk of SARA was determined based on the milk fat content and the proportions of de novo fatty acids and long chain unsaturated fatty acids in the bulk tank. On each farm, 7 early/mid-lactation (< 150 days in milk DIM) and 7 mid/late lactation (< 150 DIM) cows were randomly selected. The fatty acid profile of pooled milk samples from these cows were determined by gas chromatography. Farm risk of SARA did not affect the milk fat proportion of fatty acids, with the exception of trans 10 cis 12 C18:2, which was higher in At Risk Farms. Later lactation cows had a higher milk fat content and higher milk fat proportions of de novo, C16 fatty, and odd and branch chain fatty acids. The prevalence of SARA was likely higher in earlier lactation cows than in later lactation cows, but non-SARA related animal and dietary factors also affect the milk fatty acid profile. Hence, the milk fatty acid profile alone may not be accurate enough to diagnose SARA on farm. This profile can, however, contribute to this diagnosis, the identification of causes of milk fat depression, and the development of strategies to optimize the milk fatty acid profile.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".