Bibliographic record
Abstract
ILRI) envisions a world where all people have access to enough food and livelihood options to fulfil their potential.ILRI's mission is to improve food and nutritional security and to reduce poverty in developing countries through research for efficient, safe and sustainable use of livestock-ensuring better lives through livestock.ILRI's three strategic objectives are: i.With partners, to develop, test, adapt and promote science-based practices that-being sustainable and scalable-achieve better lives through livestock.ii.With partners, to provide compelling scientific evidence in ways that persuade decision makersfrom farms to boardrooms and parliaments-that smarter policies and bigger livestock investments can deliver significant socio-economic, health and environmental dividends to both poor nations and households.iii.With partners, to increase capacity among ILRI's key stakeholders to make better use of livestock science and investments for better lives through livestock.CGIAR ILRI is one of 15 CGIAR research centres, a global research partnership that unites organizations engaged in research for a food-secure future.CGIAR research is dedicated to reducing poverty, enhancing food and nutrition security, and improving natural resources and ecosystem services.Built on a strong partnership between CGIAR's funders and 15 centres, the governance model focuses on enabling CGIAR's centres and partners to conduct high-quality research for development based on a solid foundation of clearly defined roles, responsibilities and accountabilities.Research is carried out by the 15 centres that are members of CGIAR in close collaboration with hundreds of partner organizations, including national and regional research institutes, civil society organizations, academia and the private sector.The CGIAR Portfolio 2017-2022 emphasizes integrated agri-food systems-based approaches spanning nutrition and health, climate change, soils and degraded land, reducing food systems waste, food safety, global stewardship of genetic resources, and big data and information and communication technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".