Energy partitioning by broiler breeder pullets in skip-a-day and precision feeding systems
Bibliographic record
Abstract
An empirical nonlinear mixed model was derived to describe metabolizable energy (ME) partitioning in Ross 308 broiler breeder pullets. Its coefficients described ME used for total heat production (HP) and growth. A total of 630 pullets were randomly and equally assigned to 2 treatments: precision feeding (PF) and conventional skip-a-day feeding (CON) from 10 to 23 wk of age. The PF system allowed birds to enter voluntarily at any time, weighed them, and provided access to feed for 60 s if their BW was less than the target BW. Birds in the CON treatment were fed as a group on alternate days. Energetic efficiency of pullets was evaluated using residual total heat production (RHP), defined as the difference between observed and predicted total HP. Additionally, ME intake (MEI), ADG, HP, and cumulative feed conversion ratio (FCR) were calculated for the entire experimental period. The energy partitioning model (P < 0.05) predicted MEI = (120+u)BW0.68 + 1.52(ADG) + ε. Total HP was (120 kcal/kg0.68 + u); the energy requirement for each g of BW gain was 1.52 kcal/d. The random variable u ∼ N (0, σu2) indicated a pen level HP standard deviation σu = 12.1 kcal/kg0.68. Over the experimental period, for CON and PF treatments, respectively, MEI was 194 and 174 kcal/d (P < 0.001); ADG was 15.3 and 15.4 g/d (P = 0.94); HP was 129 and 111 kcal/kg0.68 (P < 0.001); FCR was 4.888 and 4.057 (P < 0.001); and RHP was 0.12 and -0.12 kcal/kg0.68 (P = 0.73). The CON pullets had similar ADG, but higher MEI relative to PF, consistent with levels of heat production predicted by RHP. The PF pullets had lower cumulative FCR compared to CON pullets. The PF pullets lost less energy as heat, likely because they were fed continuously, reducing the need to store and mobilize nutrients compared to CON pullets. Thus, increased feeding frequency likely increased PF pullet efficiency.
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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".