Intake of Some Biological Seeds and Root Extracts of Plants Improves Fertility and Hatchability of Turkey Eggs
Bibliographic record
Abstract
An experiment was conducted to determine the fertility and hatchability of eggs laid by Turkey hens fed extracts of okra seed, pumpkin seed and guava root powder for 8 weeks. Twenty four, 32 weeks old turkeys (4 toms and 20 hens) were randomly selected and allotted into four treatment groups; T1 (No extract or feed supplementation); T2 (50 ml okra seeds extracts/ litre of water); T3 (50 ml guava root extract/ litre of water) and T4 (50 g pumpkin seed powder/kg of feed). Turkey hens were subjected to artificial insemination and eggs laid in the period were collected and determined for fertility and hatchability. Total and weekly egg production of hens was higher (P < 0.05) in T2 and T4 groups of birds. The number of fertile eggs, early, middle and late dead embryo was better (P < 0.05) for the same groups of birds compared to other treatments. Egg hatchability percentage of hens in T2 and T4 groups were markedly improved and higher (P < 0.05) than those in T1 and T3 groups. Fertility and hatchability of eggs in T1 and T3 were similar (P > 0.05). The findings concluded that feeding okra and pumpkin seed extracts to breeder turkey hens can improves the fertility and hatchability of the eggs.
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".