365 Building bridges for sustainable livestock: The 10-year+ learnings from the Global Farm Platform
Bibliographic record
Abstract
Abstract The Global Farm Platform initiative (www.globalfarmplatform.org), established in 2014, is a network of research farms and institute members working collaboratively to enhance the sustainability of ruminant livestock systems through the development of transformational regional solutions to global challenges and promote their adoption. This multidisciplinary international network provides a unique combination of research and practice for diverse ruminant production systems in various cultural, socioeconomic, and climatic zones. Our steps for sustainable livestock include: 1) Feeding animals less human food, focusing on forage-based systems and reducing reliance on supplementary feeds; several research farms (RFs) are exploring sustainable practices, such as the Kenian “Tumbukiza” method and increased plant diversity in grazing systems, to enhance resilience and productivity. 2) Raising regionally appropriate animals adapted to local conditions; RFs are engaged in crossbreeding and selecting animals for resilience to climate change challenges, ensuring they can thrive in specific environments. 3) Keeping animals healthy; RFs employ various approaches, including technology like sensors and video cameras for monitoring animal health, as well as vaccine development to combat specific diseases. 4) Smart supplements; RFs explore options like spontaneous vegetation and innovative legumes to enrich soil quality and provide nutrition. 5) Focusing on quality over quantity in food production; RFs are implementing measures such as CT scanning for carcass confirmation and assessing the nutritional value and carbon footprint of forage-based beef systems. 6) Tailoring practices to local culture; promoting the transfer of research outcomes to stakeholders and farming communities. This includes initiatives to transform agriculture for environmental and economic benefits. 7) Tracking costs and benefits; involves assessing the environmental and economic impacts of sustainable grazing systems, utilising Life Cycle Assessment approaches, and collating databases for comprehensive analysis. 8) Studying best practices; involves the Global Farm Platform initiative’s network of research farms globally. The initiative aims to optimise livestock use in various regions, considering local resources, breeds, and feedstuffs.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".