Appetite for change: food system options for nitrogen, environment & health. 2nd European Nitrogen Assessment special report on nitrogen & food
Bibliographic record
Abstract
This report assesses the main ingredients needed to navigate the transition towards agreed nitrogen sustainability targets. \n \nGlobal nitrogen losses pose a serious threat to environmental sustainability and compromise the ability of the agricultural sector to feed a growing population. The first ENA Special Report ‘Nitrogen on the Table’ showed how encouraging more plant-based diets can promote human health and reduce nitrogen emissions. Building on these foundations, the present report ‘Appetite for Change’ explores pathways towards sustainable nitrogen and food choices. \n \nThis report, prepared by the Expert Panel on Nitrogen and Food of the UNECE Task Force on Reactive Nitrogen, presents the main ingredients and a suggested recipe to navigate the necessary sustainability transition towards agreed nitrogen targets. It shows that a combination of diet change towards plant-based diets and technical measures across the food chain can halve nitrogen waste. It thus sets out a path to reaching targets set in the Colombo Declaration, the EU Farm to Fork Strategy and the Kunming-Montreal Global Biodiversity Framework. Importantly, diet change can reduce pressure on land resources and mineral fertilizers, reduce energy dependency and increase resilience to food and energy crises. \n \n‘Appetite for Change’ emphasizes how the nitrogen cycle, food system, environment and health are inextricably interlinked. It goes on to identify ambitious and systemic actions to transform the food system. There are great opportunities for reducing nitrogen losses from food production and consumption with co-benefits for nutrition and public health. To be sustainable in the longer term, nitrogen management needs to be based on a systems approach and requires responsive governance action across inter-connected policy sectors, engaging a wide set of food system actors.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".