Feed Efficiency Estimates in Cattle: The Economic and Environmental Impacts of Reranking
Bibliographic record
Abstract
This paper proposes the application of hierarchical models to the assessment of feed efficiency in beef cattle. Using a large dataset comprising 5600 cattle assembled from different experimental studies, feed efficiency rankings of cattle were estimated using the proposed approach. This was compared to more commonly used linear, and nonlinear estimators. A phenotypic selection scheme that selects cattle at the means of different percentiles was developed to illustrate potential economic and environmental outcomes resulting from changes in feed efficiency rankings. The former involved the specification of a multi-year stochastic farm simulation model. In general, our results show that improved feed efficiency is associated with positive economic and environmental benefits. A unit reduction in feed intake (kg as fed/day) is associated with an average increase of $13.23 in net returns and 33.46 tonnes reduction in emission at the end of the feeding period. We also find that feed efficiency ranking of cattle is sensitive to estimation approach. The within percentile mean estimates of the hierarchical model were comparable to the conventional linear estimator. There were, however, deviations at the tails of feed efficiency distributions where selection is most likely to occur.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".