Energy partitioning by broiler breeder hens in conventional daily-restricted feeding and precision feeding systems
Bibliographic record
Abstract
An empirical linear mixed model was derived to describe metabolizable energy (ME) partitioning in broiler breeder hens. Its coefficients described ME used for total heat production (HP), growth (ADG), and egg mass (EM). A total of 480 Ross 308 hens were randomly and equally assigned to 2 treatments: precision feeding (PF) and conventional daily-restricted feeding (CON) from 23 to 34 wk of age. The PF system allowed birds to enter feeding stations voluntarily at any time, weighed them, and provided access to feed for 60 s if their BW was less than the breeder-recommended target BW. The CON birds were fed daily each morning. Energetic efficiency of hens was evaluated using residual feed intake (RFI), defined as the difference between observed and predicted ME intake (MEI). The energy partitioning model predicted (P < 0.05): MEI = A × BW0.67 + 1.75 × ADG + 0.75 × EM + ϵ. The coefficient A, a vector of age-specific HP, was 142 kcal/kg0.67/d; the energy requirement for growth and EM was 1.75 and 0.75 kcal/g, respectively. For the CON and the PF hens, respectively, MEI was 366 and 354 kcal/d (P = 0.006); RFI was -5.9 and 6.7 kcal/d (P = 0.009); HP% was 85.5 and 87.7 (P < 0.001); hen-day egg production (HDEP) was 65.5 and 55.2% (P < 0.001). Although the CON hens had higher MEI, the model predicted lower HP%; thus, CON hens had more nutrients available for egg production, increased egg production, and were more energetically efficient than the PF hens. The decreased egg production by the PF hens was likely due to these hens receiving production-related feed increases after an egg was laid. However, feed allocation increases for the CON hens resulted in increasing MEI for all CON hens at the same time. Therefore, the PF hens had lower MEI and lower HDEP than the CON hens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".