Intake, feeding behaviour, digestibility, performance, carcass characteristics and meat quality of lambs fed different levels of semi-purified glycerine in the diet
Bibliographic record
Abstract
The aim of this study was to evaluate the effect of different inclusion levels of semi-purified glycerine in the diet of feedlot lambs on feeding behaviour, nutrient intake and digestibility, carcass characteristics, meat quality and in vitro degradability. Thirty-two Dorper × (Texel × Suffolk) crossbred intact male lambs (22.2 ± 5.51 kg) were fed glycerine (90% purity) at 0, 120, 240 or 360 g/kg dry matter (DM) in a total mixed ration with a roughage to concentrate ratio of 40:60 for 84 d. In vitro degradability was not affected by glycerine supplementation. Feeding behaviour and digestibility of DM, crude protein and fibre and production performance were similar among treatments. Ether extract digestibility was lower at the highest inclusion level. Glycerine level had no effect on ruminal pH, carcass characteristics and meat quality, except for subcutaneous fat thickness which was lower for lambs fed glycerine at 240 and 360 g/kg DM. Scores for unpleasant taste, unpleasant odour, succulence and softness of meat were not affected by dietary glycerine level. These data suggest that there are no adverse effects on carcass quality and performance when semi-purified glycerine is provided up to 360 g/kg DM in the diet of growing lambs fed a forage to concentrate ratio of 40:60.
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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 | 0.000 |
| 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".