For all life on Earth: Actions to protect, restore and sustainably use nature. A practical guide in support of the Kumming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
Our world faces environmental crises that threaten to upend life as we know it. Earth is degraded, deforested and warming dangerously. Nature and the myriad of benefits that it provides to people is declining all around the world. Poor people and traditional societies, especially in rural areas of developing countries, are particularly vulnerable to its loss. Sustaining and restoring nature is an urgent necessity. It is also common sense. Healthy ecosystems provide our food and water and underpin our prosperity and well-being. Ecosystems shield us from hunger, drought, disease and climate catastrophe. Furthermore, they are key to realizing the rights and hopes of people everywhere to live a good life, protect their culture and lay a firm foundation for future generations. We should work with nature—not against it—to continue to enjoy its benefits and ensure a healthy future for us all. Our growing understanding of how the planet works means that we also know how to fix it. But transforming our relationship with nature requires big changes, including in government policies, the production and consumption of goods and services, and individual behaviour. This practical guide presents some of the most important actions that individuals and organizations can take to collectively achieve the goal to transform our relationship with nature.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.055 |
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".