Evaluation of three forages as a source of fiber in diets of fattening rabbits in Aguascalientes, Mexico
Bibliographic record
Abstract
Objective: To evaluate three forages as a source of fiber in the diets of fattening rabbits. Design/Methodology/Approach: Whole grain diets with forage oat, mesquite pod, and alfalfa were used. Thirty-six weaned male rabbits were randomly distributed into three treatments (T1, forage oat diet; T2, mesquite pod diet; T3, alfalfa diet). Feed consumption, daily weight gain, total weight gain, and feed conversion were recorded. The animals were slaughtered to evaluate carcass yield. The data were statistically evaluated by analysis of variance and Tukey’s test. Results: T1 recorded greater fattening than both T2 and T3 (P<0.05) and the last treatment surpassed T2 in daily weight gain, total weight gain, and feed digestibility. Regarding feed conversion, T1 and T3 had lower results than T2. In carcass yield, T1 was higher than T2 and T3 —which, on its turn, surpassed T2. Finally, no differences were observed in feed consumption between treatments (P> 0.05). There were also no significant differences in growth. Study Limitations/Implications: Mexicans have a low consumption of rabbit meat. The mesquite pod could be a viable alternative due to its low cost and availability in semi-arid areas. Findings/Conclusions: Forage oat recorded the best productive parameters, followed by alfalfa and mesquite pod; however, the latter had a greater economic advantage.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".