Restoring Value to Grassland Initiative: To Maintain the Environmental and Economic Value of Grasslands and to Promote Their Social and Cultural Functions
Bibliographic record
Abstract
The Global Agenda for Sustainable Livestock (GASL), a multi-stakeholder partnership started in 2013 includes nine action networks (ANs). The networks are the working engine of GASL and are tasked with implementing activities, reports, providing evidence, guidelines and information on good practices demonstrated by the livestock sector. This paper outlines the activities of the network AN2 “Restoring Value to Grassland”, the purpose of which is to “maintain, restore and enhance environmental and economic value of grasslands, while promoting their social and cultural functions globally”. Since 2014, AN2 workshops have been held annually with scientists and stakeholders from rangeland/grassland biomes in Latin America (Brazil, Uruguay, Argentina, Chile), the Mediterranean (France, North Africa), Sub-Saharan Africa, Highland and Continental Plateaux (Tibetan Plateau/Mongolia/Atlas in Morocco), the mountainous regions of France, New Zealand and Vietnam, and the prairie area of Canada. A data base of 40 global grassland cases and a range of preferred practices have been compiled for these areas. A methodological framework is now available for assessing the contribution of grassland systems to multiple functions, along with the development of associated indicators that are aligned with the sustainable development goals (SDGs) - social, local development, production, economic and environmental. The framework has been built and tested using the global grassland cases. We present the results from three cases from Brazil, Vietnam and Argentina.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".