Bibliographic record
Abstract
This database contains key parameters and variables from the 2023 Scenathon which has been run by the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Consortium. A scenathon - a scenario marathon - is a multi-objective challenge that allows a decentralized global modelling approach with multiple models developed by different teams in the world at national and regional scales, and a methodology to link them ensuring international trade consistency and tracking collective progress towards the achievement of global sustainability targets. A description and analysis of the Scenathon 2023 pathways has been published in Sachs et al. (2024). The Scenathon 2023 database includes results at the global, country and rest of the world regions levels, for indicators related to food and nutrition security, land and biodiversity, GHG emissions from agriculture and land use change, and input use in agriculture. It also includes key parameters that can be used to explain the results, such as the evolution of productivity and all supply and use balance items at the commodity level. It is possible to visualise some of the key results on the Scenathon dashboard. Scope of the 2023 database: Pathways: The Current Trends (CT) pathway, reflecting a low-ambition future shaped by existing policies; The National Commitments (NC) pathway, projecting how national strategies, pledges, and targets for climate, biodiversity, and food systems would shape future outcomes. The Global Sustainability (GS) pathway, identifying additional actions necessary to align national and regional pathways with global sustainability targets. Countries and regions: Argentina, Australia, Brazil, Canada, China, Colombia, Denmark, Ethiopia, Finland, Germany, Greece, India, Indonesia, Mexico, Norway, Nepal, Russia, Rwanda, Sweden, Türkiye, the UK, and the United States and the rest of the world regions Rest of Asia and Pacific, Rest of Central and South America, Rest of European Union, Rest of Europe non-EU, Rest of Sub-Saharan Africa. Time: 2000-2050 with 2020 being the last calibration year. Results are provided for each 5 year-time step. Trade adjustment: results are provided before total exports and total imports are balanced or after. The readme worksheet provides all the relevant information on the indicators and acronyms definition used in the database.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.174 | 0.087 |
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".