Levelling foods for priority micronutrient value can provide more meaningful environmental footprint comparisons
Bibliographic record
Abstract
Abstract A growing literature in Life Cycle Assessment seeks to better inform consumers, food policymakers, food supply chain actors, and other relevant stakeholders about how individual foods contribute to sustainable diets. One major challenge involves accurately capturing potential trade-offs between nutritional provision and environmental impacts associated with food production. In response, food system sustainability literature has turned increasingly to nutritional Life Cycle Assessment, which assesses the environmental footprints of different foods while accounting for nutritional value. Here we provide examples that show how environmental footprints based on a priority micronutrient-focused functional unit can provide nutritionally meaningful insights about the complexities involved in sustainable food systems. We reinforce the idea that there are limitations in using single-value nutrition-environment scores to inform food guidance, as they do not adequately capture the complex multi-dimensionality and variation involved in healthy and sustainable food systems. In our discussion we highlight the need for future agri-food sustainability assessments to pay attention to regional nutritional and environmental variation within and between commodities, and to better interpret trade-offs involved in food substitutions.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".