Influence of Dietary Nutrient Density, Feed Form, and Lighting on Growth and Meat Yield of Broiler Chickens
Bibliographic record
Abstract
The objective of this study was to examine main and interactive effects of nutrient density (ND), feed form (FF; mash, pellet), and lighting program (12L:12D, 20L:4D) on production characteristics and meat yield of broilers raised to 35 d of age. Diets (starter, grower, and finisher) were formulated so that amino acid levels were in proportion to the dietary energy level. Lighting programs were initiated at 4 d of age. Body weight was not affected by ND when diets were fed in a pellet form but decreased in a linear manner with lower ND when fed as a mash. Final BW of birds fed mash were less than those of birds fed pellet diets. Feed to gain ratio decreased with increasing ND but was not affected by FF. Feed intake decreased with increasing ND and was lower for birds fed mash. The effect of ND on feed intake was less when birds were fed mash in contrast to pellet diets (P(ND x F) < 0.0001). Dietary ND had no effect on mortality, but feeding mash decreased mortality (3.8%) compared with feeding pelleted feed (5.6%). Lighting programs affected production characteristics independently of ND and FF. Use of 12L:12D reduced BW, feed to gain ratio, feed intake, and mortality compared with 20L:4D. Similarly, carcass components were not affected by ND when fed in pellet form but decreased with lower ND when fed as a mash. Overall, carcass yields were reduced when broilers were fed mash or provided with 12L:12D. Female birds had higher carcass yields and increased proportional breast meat deposition compared with males.
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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 | 0.000 | 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".