Performance, Carcass and Meat Characteristics of West African Dwarf Rams Given Water Contaminated With Used Engine Oil
Bibliographic record
Abstract
This study was conducted to determine the effect of giving water contaminated with used engine oil on performance, carcass and meat characteristics of West African Dwarf (WAD) rams. 15 WAD rams about 10 months old were used. They were grouped into 5 balanced for weight. Used engine oil was collected from an Auto mechanic workshop in Ayetoro Yewa Ogun state and mixed to 0, 5, 10, 15 and 20ml with one litre of clean water at 0, 0.5, 1.0, 1.5 and 2.0% designated T0, T1, T2, T3 and T4 respectively. The rams were assigned to these treatment groups in a completely randomized design experiment and were given the contaminated water for 13 weeks. Data collected were subjected to analysis of variance (ANOVA) at p=0.05. The results showed that nutrient intake was higher, while water intake decreased (p<0.05) as the level of used engine oil in water increased, nitrogen intakes and urinary nitrogen decreased (p<0.05) as well as nitrogen retention. Although weight gain increased (p<0.05) feed efficiency decreased (p<0.05). Carcass primal cuts and meat characteristics decreased except cooking yield and water holding capacity as used engine oil increased in the given water. It was therefore, recommended that used engine oil should not be allowed to flow freely into the surrounding water bodies where grazing animals may consume it as this may lead to reduction in their water intake which can affect the health of the animals and increased feed intake can affect profit margin of the farmers.
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 | 0.000 |
| 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".