Assessment of residual body weight gain and residual intake and body weight gain as feed efficiency traits in the turkey (Meleagris gallopavo)
Bibliographic record
Abstract
BACKGROUND: Since feed represents 70% of the total cost in poultry production systems, an animal's ability to convert feed is an important trait. In this study, residual feed intake (RFI) and residual body weight gain (RG), and their linear combination into residual feed intake and body weight gain (RIG) were studied to estimate their genetic parameters and analyze the potential differences in feed intake between the top ranked birds based on the criteria for each trait. METHODS: Phenotypic and genetic analyses were completed on 8340 growing tom turkeys that were measured for feed intake and body weight gain over a four-week period from 16 to 20 weeks of age. RESULTS: The heritabilities of RG and RIG were 0.19 ± 0.03 and 0.23 ± 0.03, respectively. Residual body weight gain had moderate genetic correlations with feed intake (-0.41) and body weight gain (0.43). All three linear combinations to form the RIG traits had genetic correlations ranging from -0.62 to -0.52 with feed intake, and slightly weaker, 0.22 to 0.34, with body weight gain. Sorted into three equal groups (low, medium, high) based on RG, the most efficient group (high) gained 0.62 and 1.70 kg more (P < 0.001) body weight than that of the medium and low groups, yet the feed intake for the high group was less (P < 0.05) than that of the medium group (19.52 vs. 19.75 kg). When separated into similar partitions, the high RIG group (most efficient) had both the lowest (P < 0.001) feed intake (18.86 vs. 19.57 and 20.41 kg) and the highest (P < 0.001) body weight gain (7.41 vs. 7.03 and 6.43 kg) relative to the medium and low groups, respectively. CONCLUSIONS: The difference in feed intake between the top ranked birds based on different residual feed efficiency traits may be small when looking at the average individual, however, when extrapolated to the production level, the lower feed intake values could lead to significant savings in feed costs over time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".