Integrating nutritional benefits and impacts in a life cycle assessment framework: A US dairy consumption case study
Bibliographic record
Abstract
Although essential to understand the overall health impact of a food or diet, nutrition is not usually considered in food-related life cycle assessments (LCAs). As a case study to demonstrate comparing environmental and nutritional health impacts we investigate United States dairy consumption. Nutritional impacts, interpreted from disease burden epidemiology, are compared to health impacts from more tradi-tional impacts (e.g. due to exposure to particulate matter emissions across the life cycle) considered in LCAs. After accounting for the present consumption, data relating dairy intake to public health suggest that low-fat milk leads to nutritional benefits up to one additional daily serving in the American diet. We demonstrate the importance of considering the whole-diet and nutritional trade-offs. The estimated health impacts of various dietary scenarios may be of comparable magnitude to environmental impacts suggesting the need for investigat-ing the balance between dietary public health advantages and disadvantages in comparison to environmental impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".