<i>Wikipedia</i> Articles on Nutrition: Are they Accurate and Complete?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: There is controversy regarding whether Wikipedia entries in the area of health-related topics are accurate and complete. Objective: To investigate the accuracy and completeness of Wikipedia entries on nutrition. Methods: Fifty-three accurate statements were formulated. Topics covered diverse areas of nutrition. One or more search terms were developed for each statement. Wikipedia entries were identified using Google for 89 search terms. These were graded for level of accuracy and completeness. Results: The entries for 73.5% of the statements had high scores (at most only minor problems were seen). The entries for 18.9% of the statements had a lesser degree of accuracy and completeness; the most common problem was that at least one entry for a statement provided no information on the statement. Serious problems of missing information were seen with the entries for 7.6% of the statements. No errors were found in any Wikipedia entries. Conclusion: While Wikipedia entries in the area of nutrition are quite accurate and free of errors, important information is often missing. Nutrition professionals should be discouraged from relying on Wikipedia. These findings are broadly consistent with other studies of Wikipedia entries in healthrelated areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it