Environmental assessment of diets: overview and guidance on indicator choice
Bibliographic record
Abstract
Comprehensive but interpretable assessment of the environmental performance of diets involves choosing a set of appropriate indicators. Current knowledge and data gaps on the origin of dietary foodstuffs restrict use of indicators relying on site-specific information. This Personal View summarises commonly used indicators for assessing the environmental performance of diets, briefly outlines their benefits and drawbacks, and provides recommendations on indicator choices for actors across multiple fields involved in activities that include the environmental assessment of diets. We then provide recommendations on indicator choices for actors across multiple fields involved in activities that use environmental assessments, such as health and nutrition experts, policy makers, decision makers, and private-sector and public-sector sustainability officers. We recommend that environmental assessment of diets should include indicators for at least the five following areas: climate change, biosphere integrity, blue water consumption, novel entities, and impacts on natural resources (especially wild fish stocks), to capture important environmental trade-offs. If more indicators can be handled in the assessment, indicators to capture impacts related to land use quantity and quality and green water consumption should be used. For ambitious assessments, indicators related to biogeochemical flows, stratospheric ozone depletion, and energy use can be added.
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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 | 0.055 | 0.068 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".