A Whole Earth Approach to Nature-Positive Food: Biodiversity and Agriculture
Bibliographic record
Abstract
Abstract Agriculture is the largest single source of environmental degradation, responsible for over 30% of global greenhouse gas (GHG) emissions, 70% of freshwater use and 80% of land conversion: it is the single largest driver of biodiversity loss (Foley JA, Science 309:570–574, 2005, Nature 478:337–342, 2011; IPBES. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES Secretariat, Bonn, 2019; Willett W et al. The Lancet 393:447–492, 2019). Agriculture also underpins poor human health, contributing to 11 million premature deaths annually. While too many still struggle from acute hunger, a growing number of individuals, including in low to middle-income countries (LMICs), struggle to access healthy foods. Greater consideration for, and integration of, biodiversity in agriculture is a key solution space for improving health, eliminating hunger and achieving nature-positive development objectives. This rapid evidence review documents the best available evidence of agriculture’s relationships with biodiversity, drawing on the contributions of leading biodiversity experts, and recommends actions that can be taken to move towards more biodiversity/nature-positive production through the delivery of integrated agricultural solutions for climate, biodiversity, nutrition and livelihoods. The analysis, which takes a whole-of-food-system approach, brings together a large body of evidence. It accounts for aspects not typically captured in a stand-alone primary piece of research and indicates where there are critical gaps.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".