Associations of Blood Analysis with Feed Efficiency and Developmental Stage in Grass-Fed Beef Heifers
Bibliographic record
Abstract
Proxies for feed efficiency, such as blood-based indicators, applicable across heifers varying in genetic makeup and developmental state are needed. Assessments of blood analytes and performance were made in heifer calves and pregnant heifers. Residual feed intake, a measure of feed efficiency, was used to categorize each population of heifers as either efficient or inefficient. Efficient heifer calves had lower mean cell hemoglobin, greater lymphocyte count, and fewer segmented neutrophils at the end of the test compared to inefficient calves. Efficient pregnant heifers had greater counts of lymphocytes with fewer segmented neutrophils at the end than inefficient pregnant heifers. Efficient heifer calves exhibited higher specific immunoglobulin M than inefficient calves. Throughout the test, efficient heifer calves had elevated potassium and phosphorus, and reduced alkaline phosphatase (ALP) compared to inefficient heifers. Efficient pregnant heifers showed greater ALP, non-esterified fatty acids and creatinine, but lower cholesterol and globulin than inefficient pregnant heifers. Levels of red and white blood cells, creatine kinase, cholesterol, glucose, potassium and phosphorus were higher in heifer calves compared with pregnant heifers. There is potential for blood analytes as proxies for feed efficiency; however, it is necessary to consider the inherent associations with feed efficiency and heifers' developmental stage.
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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 | 0.000 | 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) | 0.000 | 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".