Pre-Weaning Growth Performance of Piglets at Smallholder Farms in Gauteng Province
Bibliographic record
Abstract
The objective of the study was to determine pre-weaning performance of piglets born following artificial insemination (AI) at smallholder farms of Gauteng province. Data from 496 piglets originating from 73 multiparous crossbred sows were used in the study. Litter size, number of piglets born alive, number of piglets weaned, birth and weaning weights were recorded. Data was analysed using the Proc Univariate procedure of SAS. The average litter size was 11.8. The average birth weight and weaning weights were 1.9 and 6.2 kg, respectively. No significant differences were found between male and female piglets for all the growth performance characteristics. Piglets born during winter had a significantly higher (P < 0.05) birth and weaning weight as compared to autumn and summer months. Season had a significant effect on birth and weaning weight (P < 0.01). However, sex of piglets had no significant effect on all the characteristics recorded (P > 0.05). The interaction between sex and season was only confirmed on the total number of weaned piglets (P < 0.01). A highly significant positive correlation was found between litter size and number of piglets born alive (r = 0.86) and total number of piglets weaned (r = 0.50). A highly significant correlation was found between total number of piglets born alive and total number of piglets weaned (r = 0.55). In conclusion, season of birth had the greatest impact on birth and weaning weight, with the highest birth and weaning weights recorded during winter season. However, sex did not affect the pre-weaning performance of piglets.
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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 | 0.001 | 0.001 |
| 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".