Nutritional quality and climate impact of Norwegian adults’ diet classified according to the NOVA system
Bibliographic record
Abstract
Information about how ultra-processed foods (UPF) contribute to the intake of energy and nutrients and environmental impact is important for future food policies and dietary recommendations. This study assessed the contribution of the four NOVA food groups, including UPF, to energy intake, nutritional quality, and climate impact in Norwegian adults' diet. We used dietary data from 348 participants in the Norkost 4 pilot study, collected through two non-consecutive 24-h dietary recalls. Foods, beverages, and dietary supplements were classified using the NOVA system. The NOVA system has four groups; NOVA 1: Unprocessed or minimally processed foods, NOVA 2: Processed culinary ingredients, NOVA 3: Processed foods, and NOVA 4: UPF. UPF contributed to 48% of the total energy intake, NOVA 3 with 19%, and NOVA 1 with 28%. Within UPF, bread contributed the most to energy intake. Foods in NOVA 1 had the highest nutrient density (expressed as amount of nutrients provided per unit of energy) for protein, fiber, and several essential micronutrients. UPF had the highest nutrient density for added sugar, fats, and sodium. UPF contributed to 32% of total GWP from the diet, while NOVA 1 contributed to 38%. In conclusion, UPF contributed to about half of the energy intake of Norwegian adults, and had lower nutritional quality compared to NOVA 1. UPF also accounted for about one-third of the GWP from the total diet. These findings emphasize the importance of addressing the intake of UPF in dietary policies and recommendations to improve nutritional quality and reduce environmental impact.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".