Carbon dioxide (CO2) emissions and adherence to Mediterranean diet in an adult population: the Mediterranean diet index as a pollution level index
Bibliographic record
Abstract
Background Research related to sustainable diets is is highly relevant to provide better understanding of the impact of dietary intake on the health and the environment. Aim To assess the association between the adherence to an energy-restricted Mediterranean diet and the amount of CO2 emitted in an older adult population. Design and population Using a cross-sectional design, the association between the adherence to an energy-reduced Mediterranean Diet (erMedDiet) score and dietary CO2 emissions in 6646 participants was assessed. Methods Food intake and adherence to the erMedDiet was assessed using validated food frequency questionnaire and 17-item Mediterranean questionnaire. Sociodemographic characteristics were documented. Environmental impact was calculated through greenhouse gas emissions estimations, specifically CO2 emissions of each participant diet per day, using a European database. Participants were distributed in quartiles according to their estimated CO2 emissions expressed in kg/day: Q1 (≤2.01 kg CO2), Q2 (2.02-2.34 kg CO2), Q3 (2.35-2.79 kg CO2) and Q4 (≥2.80 kg CO2). Results More men than women induced higher dietary levels of CO2 emissions. Participants reporting higher consumption of vegetables, fruits, legumes, nuts, whole cereals, preferring white meat, and having less consumption of red meat were mostly emitting less kg of CO2 through diet. Participants with higher adherence to the Mediterranean Diet showed lower odds for dietary CO2 emissions: Q2 (OR 0.87; 95%CI: 0.76-1.00), Q3 (OR 0.69; 95%CI: 0.69-0.79) and Q4 (OR 0.48; 95%CI: 0.42-0.55) vs Q1 (reference). Conclusions The Mediterranean diet can be environmentally protective since the higher the adherence to the Mediterranean diet, the lower total dietary CO2 emissions. Mediterranean Diet index may be used as a pollution level index.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".