Molecular basis for residual feed intake in beef cattle1
Bibliographic record
Abstract
Feed provision is one of the greatest costs of beef production and, with the increasing costs of feed, will remain so for the foreseeable future. Improvement in efficiency has the potential to not only increase profits for cattle producers, but also to decrease the environmental footprint of beef cattle production. Both are important in addressing the challenges of increasing feed costs and land pressure. Residual feed intake (RFI) has increasingly become the measure of choice when evaluating feed efficiency in beef cattle, especially because it is independent of growth and BW. The main inhibitor to adoption of RFI remains the cost and technical difficulty in measuring the trait. This makes RFI a prime candidate for marker-assisted selection because the trait is moderately heritable and DNA or other predictive markers could be used in selection schemes. Although multiple markers have been described over several studies, no major gene affecting RFI has been found. However, a combination of genetic markers, when examined jointly, can explain a large proportion of the genetic variation. Two main barriers remain before full adoption of markers for genetic evaluation and marker-assisted selection can be implemented. First, the genetic interaction of genes affecting RFI on other traits is, as yet, not fully understood. Second the numbers of animals with high quality estimates of RFI remains small. However, current developments indicate that these challenges will soon be overcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".