<i>Wikipedia</i> Articles on Nutrition: Are they Accurate and Complete?
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.
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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 | 0.016 | 0.184 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".