The relationship between feed efficiency traits and fertility in young beef bulls
Bibliographic record
Abstract
Awda, B. J., Miller, S. P., Montanholi, Y. R., Vander Voort, G., Caldwell, T., Buhr, M. M. and Swanson, K. C. 2013. The relationship between feed efficiency traits and fertility in young beef bulls. Can. J. Anim. Sci. 93: 185–192. Great efforts have been taken in recent years for improving feed efficiency in beef cattle. Despite there being several studies on biological factors associated with this trait, little is known about the potential relationships between fertility and feed efficiency in beef cattle. The objective of this study was to examine the relationship between feed efficiency traits and bull fertility traits [sperm motility, viability and scrotal circumference (SC)]. From a total of 328 crossbred beef bulls that were subjected to a performance test (112 d in each of 6 yr), SC was measured and semen collected from 110 bulls using the electroejaculation method (average age±SE=417±2.5 d). Sperm were extended, cooled, and frozen in liquid nitrogen. Two residual feed intake (RFI) measures were considered with different prediction models for dry matter intake (DMI), RFIKoch included size and growth rate and RFIbkft included the additional adjustment for backfat thickness (BKFT). Sperm viability, motility and progressive motility of the 10 bulls with the greatest RFIKoch (Hi-RFIKoch) were greater than those of the 10 bulls with the lowest RFIKoch (Lo-RFIKoch; P<0.05, 0.01, and 0.05, respectively). Sperm motility (P<0.01), progressive motility and SC (P<0.05) of the 10 bulls with the greatest RFIbkft (Hi-RFIbkft) were greater than those of the 10 bulls with the lowest RFIbkft (Lo-RFIbkft). In summary, these data indicate that young beef bulls with greater feed efficiency have decreased sperm motility, sperm viability and SC, which is an undesirable effect of selection for improved feed efficiency that needs to be addressed through multiple trait selection.
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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 | 0.000 | 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) | 0.001 | 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".