Applications of Technology for Livestock for Small Holders to Meet Global Food Production
Bibliographic record
Abstract
While the Green Revolution greatly changed production of crops worldwide and helped feed over a billion people, improved production of livestock has been more limited across the globe. Modern advances in livestock production have in general only benefitted two groups: large scale livestock producers and consumers in the developed world. In some parts of the world many of the animal production practices have not changed for the last 1000 years and in other regions small holders have benefited only marginally by the scientific advances that now are an integral part of large scale commercial production. However, increased food insecurity and a worldwide food production crisis loom in the future as the most significant scientific challenge facing us in the next 30 years. Expectations are that human population growth will soon go from 7.3 billion to 9.6 billion by 2050, and food production must increase rapidly to meet the demand. These increases must come despite evidence of climate change and limited land and water resources. Whilst there is a perception of over consumption of animal products in the developed world, there are still significant numbers that are undernourished. Animal protein plays a very important role in achieving a balanced diet in the developing world, especially for the adequate nutrition of children. Furthermore, it is expected that significant increased demand for animal source foods will occur especially in the poorer countries of the developing world where most livestock are produced by small holders, but also in countries like China and Brazil which are transforming quickly. Many researchers have touted that modern feeding systems, and the use of advanced reproductive technologies and advanced genetics and genomics will provide solutions to increasing food in the developing world. These opportunities certainly exist, but direction and focus of research, funding issues, human capacity training and training of small holders will all be required for increasing livestock production to satisfy these demands. These activities will need to be embedded within sustainable programs that address implementation from the outset, and benefiting small holder production will be crucial to meeting this challenge.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".