Integrated modeling to achieve global goals: lessons from the Food, Agriculture, Biodiversity, Land-use, and Energy (FABLE) initiative
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Humanity is challenged with making progress toward global biodiversity, freshwater, and climate goals, while providing food and nutritional security for everyone. Our current food and land-use systems are incompatible with this ambition making them unsustainable. Papers in this special feature introduce a participatory, integrated modeling approach applied to provide insights on how to transform food and land-use systems to sustainable trajectories in 12 countries: Argentina, Australia, Canada, China, Germany, Finland, India, Mexico, Rwanda, Sweden, the UK, and USA. Papers are based on work completed by members of the Food, Agriculture, Biodiversity, Land-use, and Energy (FABLE) initiative, a network of in-country research teams engaging policymakers and other local stakeholders to co-develop future food and land-use scenarios and modeling their national and global sustainability impacts. Here, we discuss the key leverage points, methodological advances, and multi-sector engagement strategies presented and applied in this collection of work to set countries and our planet on course for achieving food security, biodiversity, freshwater, and climate targets by 2050.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it