Pathways for food and land use systems to contribute to global biodiversity targets
Bibliographic record
Abstract
The brief focuses on the achievements of the following three global biodiversity targets from the CBD post-2020 framework (CBD/WG2020/3/3), by 2030 and 2050: Enhance the integrity of all ecosystems, "with an increase of at least 15% in the area, connectivity, and integrity of natural ecosystems, supporting healthy and resilient populations of all species" by 2050. Achieve a "net gain in the area, connectivity, and integrity of natural systems of at least 5%" by 2030. Retain "existing intact and wilderness areas", halting losses by 2030 or before. The study develops the modelling of two possible future scenarios for the 20 FABLE countries: Argentina, Australia, Brazil, Canada, China, Colombia, Ethiopia, Germany, Finland, India, Indonesia, Malaysia, Mexico, Norway, Russia, Rwanda, South Africa, Sweden, the United Kingdom and the United States.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".