Optimization of the feed intake test period in sheep production
Bibliographic record
Abstract
Measuring feed efficiency (FE) in sheep is costly. Reducing the duration of dry matter intake (DMI) tests as a component of FE may lower expenses, while allowing for more animals to be assessed annually. This study analyzed a dataset of 46 016 DMI values from 1064 animals (796 ewes and 268 rams) in nine contemporary groups to explore the feasibility of shortening the DMI measurement periods from the 38-day standard DMI test period to accurately predict FE. We assessed the accuracy and precision of shortened test periods using Pearson and Spearman correlations, standardized regression coefficients (β), coefficient of determination ( R2), and concordance correlation coefficients (CCC). Results indicated that DMI test period can be reduced from the 38 days standard test days to 22, 24, and 25 days in ewes, rams and across gender, respectively, while maintaining Pearson and Spearman correlations of 95%. Other statistics revealed that the DMI test period in ewes could be reduced from 38 to 22 days with a β of 0.89, R2 of 0.94, and CCC at 95%, to 24 days for rams with β of 0.96, R2 of 0.94, and CCC at 0.95, and to 25 days across gender with β of 0.97, R2 of 0.96, and CCC at 0.97. In conclusion, DMI test periods can be shortened without compromising data integrity, enhancing phenotypic evaluations in sheep.
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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 | 0.005 |
| 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 | 0.001 | 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 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".