Performance, carcass characteristics, and centesimal composition of meat from Santa Inês lambs and Texel crossbred lambs (Santa Inês × Texel)
Bibliographic record
Abstract
This study evaluated the performance, carcass characteristics, and centesimal composition of the meat of intact and castrated lambs of the Santa Inês breed and of the crossbred animals: one-half Santa Inês × one-half Texel. Thirty-four lambs were used, nine intact and nine castrated males of the Santa Inês breed, and seven intact and nine castrated crossbred males, all aged between 6 and 7 mo and with an average live weight of 18.2 kg. The design was completely randomized in a 2 × 2 factorial (two crossing and two sexual conditions), with statistical analyses performed in the STAT version 9.4 program. There was a significant difference the Santa Inês and the Texel and Santa Inês crossbred animals in feed conversion and average daily gain, with superiority of the latter group. Castration of animals aged <12 mo had no significant effect on the performance and on the carcass traits of the Texel and Santa Inês crossbred animals, whereas the genotype had a great influence on weight gain and on subjective and quantitative carcass characteristics, demonstrating that crosses in sheep are needed to explore the complementarity of breeds, and heterosis is needed to achieve better performances and carcass characteristics.
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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 | 0.001 | 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".