Optimizing feed intake recording and feed efficiency estimation to increase the rate of genetic gain for feed efficiency in beef cattle
Bibliographic record
Abstract
Data from a total of 4842 animals were used to test whether the regular dry matter intake (DMI) data collection and residual feed intake (RFI) estimation period could be decreased. Eighty-three shortened test periods were compared with the regular test period, and the results showed that the DMI data collection period could be decreased to 42 d without significantly compromising accuracy of feed efficiency testing. Competency of the selected shorter period (42 d with 30–42 d of valid feed intake days) to predict regular test period DMI (84 d with 60–84 d of valid feed intake days) was tested using a set of agreements criteria. The results showed that the selected shorter period can be used to accurately and precisely predict regular test DMI. The selected shorter test period combined with regular body weight measurements were used to estimate RFI adjusted for backfat (RFIfat). Assessment of agreement between estimated values for RFIfat showed that a shorter DMI test could be used to predict RFIfat with only 7% outside the range prediction. It is concluded that shortening the feed intake period to 42 d from 84 d could substantially increase power-of-the-test for experiments that target feed intake or efficiency and reduce per head cost with the current infrastructure.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".