Effect of arginine supplementation of broiler breeder hens on progeny performance
Bibliographic record
Abstract
Fernandes, J. I. M., Murakami, A. E., Gomes de Souza, L. M., Ospina-Rojas, I. C. and Rossi, R. M. 2014. Effect of arginine supplementation of broiler breeder hens on progeny performance. Can. J. Anim. Sci. 94: 313–321. Two experiments were conducted to determine the effects of arginine (Arg) supplementation of broiler breeder hens on the performance, carcass yield, and bone measurements of their progeny. In both experiments, the maternal diet was supplemented with five levels of digestible Arg (0.94, 1.09, 1.24, 1.39 and 1.54%). In exp. 1, a total progeny of 1050 chicks were housed in pens according to maternal diet and fed a typical diet without L-Arg supplementation. In exp. 2, a total progeny of 960 chicks were kept in pens according to maternal diet and fed diets containing supplemental L-Arg from 1.30 to 1.90% in the starter phase and from 1.15 to 1.75% in the grower phase. The data obtained in both experiments were deployed in orthogonal polynomials to allow for an analysis of variance and a regression analysis. In the starter phase, there was a quadratic effect (P<0.05) of Arg level in the maternal diet on the feed:gain ratio of the non-supplemented progeny. In the Arg-supplemented progeny, there was a quadratic effect (P<0.05) of Arg level on the feed intake and feed:gain ratio and a linear increase (P<0.05) in body weight gain, and carcass and breast yields (P<0.05). Femur length, tibia diameter, and the Seedor index of both bones increased linearly (P<0.05) in broilers fed the Arg-supplemented diet. Arg supplementation in the broiler breeder hen diets had little positive effect on the non-supplemented progeny; thus, Arg supplementation in the progeny diet is necessary to optimize performance, carcass yield, and bone quality of these hens’ progeny.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".