Effects of Guar (<i>Cyamopsis tetragonoloba</i>) Residues on the Performance and Nutrients Digestibility in Finishing Awassi Lambs
Bibliographic record
Abstract
The nutritive quality of guar (Cyamopsis tetragonoloba) residues (GR) was investigated and the effects of partial replacement of wheat straw by GR on nutrients intake, digestibility, and growth performance. Twenty four male lambs, with average body weight (BW) of 40.3 ± 2.5 kg were divided into three groups. Lambs were fed individually for 104 days with concentrate mixture (18% crude protein) and wheat straw as a total mixed ration. Wheat straw was replaced with 0, 100 and 145 g/kg dry matter guar residues. All rations were isonitrogenous and isocaloric. The fattening trial lasted 104 days. At the end of week 10 of the trail, a digestion trial was performed using six lambs from each group. The composition of nutrients in the GR was 87%, 8.5%, 56.4% and 40.1% for DM, CP, NDF and ADF, respectively. DM and crude protein (CP) intake were not affected by the inclusion of GR. However, neutral detergent fibre (NDF) and acid detergent fibre (ADF) intake were lower in lambs fed GR compared to control lambs. At the end of the experiment, lambs fed 100 and 145 g GR/kg DM diets gained more weight (P version (CR). Results from this work suggested that GR had advantages compared to regular roughage (wheat straw) in regard to parameters investigated as well as the significant reduction in fattening diets cost.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".