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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".