Bibliographic record
Abstract
The potential of grasscutters (Thryonomys swinderianus temminck) as a source of animal protein can be exploited with better understanding of its nutrient requirement. An experiment was conducted to determine the protein requirement of lactating grasscutters fed agro-industrial by-products namely; wheat offal and soybean meal. Sixteen 13 months old lactating grasscutters, in groups of four, were randomly allotted to four treatment diets formulated to respectively supply 10, 14, 18 and 22% crude protein (CP). Performance in respect of weight of does at end of lactation, daily weight gain of pups, daily weight gain of doe and litter, weaning weight of pups, feed conversion ratio, and cost to gain ratio, were significantly (P<0.05) higher on the 22% CP diet. The daily weight loss of does and percentage mortality among pups were significantly lower on the 22% CP diet. Though the percentage mortality among pups was significantly (P<0.05) higher, the litter size weaned was significantly (P<0.05) higher on the 18% diet. Given the overall economic importance of low mortality rate in the expansion of farm animal populations and profitability thereof, these results suggest that 22% is the optimum crude protein level for lactating grasscutters, when industrial by-products, soybean meal and wheat offal, are used as dietary supplements.
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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".