Climate/Environment, Health, Agriculture, and Improved Nutrition
Bibliographic record
Abstract
Objectives: This study had the following aims: 1) to assess the environmental sustainability of the three healthy diet patterns recommended in the 2015-2020 Dietary Guidelines for Americans (DGA); and in so doing 2) to illustrate the value that life cycle thinking and methods can bring to the development of dietary guidance. Methods:We analyzed the Healthy US (US), Healthy Mediterranean-Style (MED), and Healthy Vegetarian (VEG) patterns.Food groups and subgroups included nutrient-dense versions of >320 commonly consumed foods, with group composition predetermined by the USDA based on food pattern modeling.To estimate the life-cycle impacts of these foods, we adapted the database underlying a new tool that combines nutrient density and environmental impacts, the Dietary Environmental Index (DEX).Specifically, we expanded the boundary of DEX's environmental dataset to focus on 6 categories of critical policy importance: global warming potential (climate change impact), land use, water depletion, freshwater and marine eutrophication (water quality impacts), and particulate matter/respiratory organics (air quality impact).Results: The US and MED patterns had similar impacts, except for freshwater eutrophication.Freshwater eutrophication was 45% higher in the MED pattern, primarily due to increased seafood recommendations.All three patterns had comparable water depletion impacts.Fruits and vegetables, which are recommended in similar amounts across patterns, were major contributors to water depletion.For the other 5 impacts, the VEG pattern had dramatically lower burdens (42-84%) than both the US and MED patterns.The reliance on plant-based protein and eggs in the VEG pattern compared with the emphasis on animal-based protein in the other patterns was a key driver of differences.Conclusions: The recommended patterns in the DGA may have starkly different implications for the environment and other dimensions of human health.Continued interdisciplinary dialog and collaboration are therefore necessary to develop a food policy that promotes long-term food security by considering the sustainability of recommended diets.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".