A European carotenoid database to assess carotenoid intakes and its use in a five-country comparative study
Bibliographic record
Abstract
A food frequency questionnaire (FFQ) and carotenoid database with information on alpha- and beta-carotene, lutein, lycopene and beta-cryptoxanthin was prepared and used to compare the carotenoid intakes in five European countries: UK, Republic of Ireland, Spain, France and The Netherlands. Eighty, age- (25-45 years) and sex-matched volunteers were recruited in each of the five countries. A FFQ and carotenoid database was prepared of the most commonly consumed carotenoid rich foods in the participating countries and the information was used to calculate frequency and intake of carotenoid-rich foods. The median total carotenoid intake based on the sum of the five carotenoids, was significantly higher (P < 0.05) in France (16.1 mg/day) and lower in Spain (9.5 mg/day,) than the other countries, where the average intake was approximately 14 mg/day. Comparison of dietary source of carotenoids showed that carrots were the major source of beta-carotene in all countries except Spain where spinach was most important. Likewise, carrots were also the main source of alpha-carotene. Tomato or tomato products, were the major source of lycopene. Lutein was mainly obtained from peas in Republic of Ireland and the UK, however, spinach was found to be the major source in other countries. In all countries, beta-cryptoxanthin was primarily obtained from citrus fruit. Comparing the data with that from specific European country studies suggests that the FFQ and carotenoid database described in the present paper can be used for comparative dietary intake studies within Europe. The results show that within Europe there are differences in the specific intake of some carotenoids which are related to different foods consumed by people in different countries.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".