Artificial Insemination Training Program for Smallholder Pig Farms in Gauteng Province, South Africa
Bibliographic record
Abstract
The aim of this study was to facilitate artificial insemination training to enhance sustainable pig production within the developing smallholder pig production sector in Gauteng Province, South Africa. Eighteen smallholder pig farmers with requisite structures (pig house, pens), pigs (large white, landrace duroc or South African indigenous) and management (feeding, cleaning and record keeping) capacity were trained on routine pig management and artificial insemination procedures in a “learning by doing” on-farm supervised programme administered by Agricultural Research Council, Animal Production pig training team. Following estrus detection, 96 sows were artificially inseminated and 31 naturally served (NS). Farrowing rates (FR), total born (TB) and born alive (BA) piglets were recorded. The occurrence ccurrence of mummified fetuses (0.019 vs. 0.022%) and weak piglets (0.038 vs. 0.049%) did not differ between artificially inseminated sows and naturally mated sows. Born alive, birth weight and weaning weight were higher for artificial inseminated sows. The average litter size was 15± and 13±, birth weight 1.98±0.79 kg and 1.48±0.58 kg and weaning weight 9.89±0.87 kg and 7.23±0.71 kg for the AI and NS litters, respectively. Farmer demographic factors (age, gender and educational level) had no effect on farrowing rate, total born and piglets born alive. Therefore, implementation of artificial insemination techniques and pig production training was feasible under a smallholder pig production system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".