Effect of Different Breeding Densities and Inclusion of Soybean Oil on Performance, Carcass Trait and Heat Loss in Meat Quails
Bibliographic record
Abstract
Quails are homeothermic animals, in countries with a tropical climate, such as Brazil, birds face great difficulties in controlling body temperature inside the sheds. Several practices are being studied in order to keep these animals within the thermal comfort zone, and thus, to minimize the effects of thermal stress, partial substitution of carbohydrates by oils and changes in density are some of them. The objective of this study was to evaluate the different breeding densities and inclusion levels of soybean oil in performance, carcass characteristics and heat loss in quail. 450 quails, seven days old, distributed in a completely randomized design in a 3 × 2 factorial scheme, two densities (215.6 cm2 bird-1 and 188.6 cm2 bird-1) and three inclusion levels of soybean oil (0, 2, 4%) were used, totaling six treatments with five replicates each. The heat loss by radiation was estimated with the aid of infrared thermography. The results showed that there was an effect of the density on feed consumption, which was lower for birds raised in the density of 188.6 cm2 bird-1 (783.78 g of feed), but without its effect on weight gain and food conversion. No effect of inclusion levels of soybean oil was observed on any performance variables. There was no statistical difference (P > 0.05) between treatments for any variables of carcass yield and parts, and heat loss by radiation analyzed. Therefore, it is concluded that soybean oil inclusion levels do not affect the performance and yield of quails, nor do they influence the loss of heat throughout the life cycle of these animals. The birds raised in the density of 188.6 cm2 bird-1 obtained the lowest average dietary intake, with no impact on weight gain and feed conversion, allowing the creation of a greater number of birds per box.
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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 | 0.001 |
| 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".