The Role of Modern Technologies for Improving the Production Environment of Livestock in Africa
Bibliographic record
Abstract
Abstract Technology was instrumental in advancing human civilization, and present-day modern technologies are enabling breakthrough innovations in medicine, agriculture, and other science sectors in Western countries. Current levels of livestock productivity in Africa are suboptimal and marred by a myriad of problems requiring urgent intervention. Modern technologies, known to be instrumental in advancing the livestock sector in western countries, will play a big role in supporting rapid growth in the African livestock sector. This chapter presents modern technologies that have advanced many aspects of livestock management in recent decades. First, Section 21.2 presents the technologies used for assessment and improvement of production efficiencies followed by examination of the technologies in animal welfare management (Section 21.3), while some of the technologies enhancing meat production, processing, and preservation are presented in Section 21.4. This is followed by an overview of the technologies for livestock nutrition and feeding (Section 21.5), livestock health management (Section 21.6), housing and milking management (Section 21.7), and technologies in pastoral livestock production systems (Section 21.8). To fully benefit from these technologies, current challenges (Section 21.9) must be addressed to pave the way for accelerated improvements in the African livestock sector following their adoption and implementation.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".