Genetic improvement and prediction of dry matter intake in beef bulls
Bibliographic record
Abstract
Phenotypic evaluations of various models for the prediction of Dry Matter Intake (DMI) were performed. Genetic parameters were estimated for DMI, Average Daily Gain (ADG), Back Fat thickness (BF), Metabolic Mid-test Weight (MW), Test Weight (TWs), five predicted DMI phenotypes and five definitions of Residual Feed Intake (RFI). These genetic parameters were used to determine response in DMI under various selection scenarios including multiple trait selection with varying sources of data and RFI based selection approaches. It was determined that simple linear regression models with parameters estimated on the data of interest are appropriate for generating predicted DMI phenotypes for use in RFI or as indicators of DMI. Results from different selection scenarios indicate that selection for reduced DMI using a multiple trait index and data on DMI, ADG, BF, MW and TWs will generate the most genetic change in a breeding objective which places all economic value on DMI.
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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 | 0.004 | 0.007 |
| 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 | 0.001 | 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".