Effect of body weight on the carcass composition of French Lop rabbits
Bibliographic record
Abstract
The experiment was performed on 60 male French Lop rabbits reared under extensive conditions and sacrificed at body weights of approximately 3 kg at the age of 150 d (30 animals) and approximately 4.5 kg at the age of 210 d (30 animals). Chilled carcasses without heads were divided into the front, middle and hind sections, which were then dissected to separate lean meat (including intramuscular fat), fat and bones. An increase in the body weight of rabbits at slaughter was accompanied by a decrease, of about 0.60%, in the proportion of the head and giblets (kidneys, liver, heart and lungs) in the carcass, and by an increase in the perirenal fat content from 0.66 to 1.69%. The average carcass dressing percentage of rabbits sacrificed at an average body weight of 3054 g reached 49.13%, and it was 2.49% higher than in rabbits slaughtered at a body weight of 4427 g. The percentage content of the front, middle and hind sections of the carcasses of the lighter rabbits was 38.50, 21.76 and 39.74%, respectively. In the carcasses of the heavier rabbits, the proportion of the front section was 2.29% higher, the proportion the hind section was 2.45% lower, while the proportion of the middle section remained at a similar level as in the lighter rabbits. The carcasses of the lighter rabbits, compared with the carcasses of the heavier rabbits, had a higher percentage content of meat (82.60 vs. 81.15%; P ≤ 0.01) and a lower percentage content of fat (1.78 vs. 4.38%). In addition, rabbits sacrificed at a body weight of approximately 3.0 kg were marked by a higher content of lean meat in the middle and front sections of the carcass (by 1.89 and 3.07%, respectively), and by a slightly lower content of lean meat in the hind section (by 0.85%). Key words: Rabbit, body weight, slaughter quality
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".