228 President Oral Presentation Pick: Milk biomarkers for determining the incidence of sub-acute ruminal acidosis
Bibliographic record
Abstract
Abstract In order to test the validity of milk amyloid A (MAA) as a biomarker for inflammation and subacute ruminal acidosis (SARA), 320 milk samples from 24 commercial dairy farms in Quebec were tested for milk amyloid A using a commercial kit. These farms were divided into low risk of SARA farms and high risk of SARA farms according to the proportions of short chain and polyunsaturated fatty acids content in the bulk tank of the farms. It was assumed that farms at risk of SARA had a lower proportion of short chain and a higher proportion of polyunsaturated fatty acids compared to farms that were not at risk of SARA. Farms were also blocked in groups of two farms by geographical location and management. Each block included an at-risk and a not-at-risk farm. On each farm, 7 early- to mid-lactation and 7 mid- to late-lactation cows were randomly selected for MAA analysis. Cows with a somatic cell count (SCC) of over 200,000 in pooled milk samples were not included. Data were analyzed using SAS Proc. Mixed with Stage of lactation and Risk of SARA as fixed factors, and Block as a random factor. The model also included somatic cell counts (SCC) and parity as a covariates. The concentrations of MAA ranged from non-detectable, i.e. below 0.1 ug/ml, to 3267.9 ug/ml with an average of 336.28 ug/ml.The effects of Block, Stage of lactation, and Risk of SARA on MAA were not significant. However, SCC and parity were significantly (P < 0.01) correlated (P < .001) with MAA with correlation coefficients of 0.34 and 0.26, respectively. The results show that measurement of MAA may be a suitable biomarker for subclinical inflammations such as subclinical mastitis, but not for the Risk of SARA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.189 | 0.057 |
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".