The diagnosis of sub-acute ruminal acidosis (SARA) on commercial dairy farms using milk fatty acid profile and milk amyloid A
Bibliographic record
Abstract
The objective of this research was to determine whether the fatty acid (FA) profile and the concentration of milk amyloid A (MAA) in milk from individual cows can be used to diagnose sub-acute ruminal acidosis (SARA) on commercial dairy farms. The use of the milk FA profile and milk amyloid A (MAA) to diagnose SARA has been validated in experimentally induced grain-based SARA challenges. A total of 320 milk samples from 24 commercial dairy farms in Quebec were tested for milk FA profile using gas chromatography method and for MAA using a commercial ELISA kit. Farms were divided into low SARA risk farms and high SARA Risk farms. High SARA Risk farms had a proportion of de novo FAs below 0.89 g/100 g of milk, a proportion of polyunsaturated FAs (PUFAs) greater than 3.40 g/100 g of total FAs, and a milk fat content below 4.00%, and a milk protein content above 3.05% in bulk tank milk. Low SARA Risk farms had a de novo FA content of milk higher than 1.07 g/100 g of milk. There were 12 blocks with each block containing a high SARA Risk farm and a low SARA Risk farm. On each farm, 7 early to mid-lactation (1 – 150 DIM) and 7 mid to late lactation cows (151 and more DIM) were randomly selected. Cows with a somatic cell count (SCC) of over 200,000 cells/ml were not included. Data were analyzed using SAS Proc Mixed with Cow risk of SARA and Farm Risk level of SARA as fixed factors, and Block as a random factor. The model for MAA also included somatic cell counts (SCC) and parity as covariates. The Farm Risk of SARA did not affect the milk fat proportions of FAs. The effects of Cow Risk of SARA and Farm Risk of SARA on MAA were not significant. The milk FA profile can contribute to the diagnosis of SARA, the identification of causes of milk fat depression, and the development of strategies to optimize the milk FA profile.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".