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
Substantial numbers of children and adults report having immunoglobulin E–mediated food allergies. However, generating accurate food allergy prevalence data is difficult. Self-reported data can overestimate prevalence when compared with prevalence estimates established by more rigorous methods. As of 2004, in the United States, the Food Allergen Labeling and Consumer Protection Act mandated that the label should declare the source of the food if the product contains that food or a protein-containing ingredient from that food (not all proteins in a major food allergen cause allergic reactions) in the manner described by the law. The 8 major food allergens are milk, eggs, fish, crustacean shellfish, tree nuts, peanuts, wheat, and soybeans, commonly referred to as the “Big 8.” These 8 allergens are thought to account for 90% of the food allergy reactions. Recently published large surveys of Americans and Canadian adults and children provide considerable insight into the prevalence of allergy for the major allergens. These data indicate that there is a large variation in prevalence among the Big 8. The prevalence of soy beans allergy is lower than the prevalence reported for each of the other 7 major allergens, which has been used to argue that soy could be removed from the Big 8 without risking harm to the public. However, the momentum appears to be in favor of expanding the Big 8. The US Food and Drug Administration is evaluating classification of sesame seed as a major allergen; it is already classified as a major allergen in Canada, Australia, and Europe. Europe classifies 14 foods as major allergens. There may be some advantage to standardizing major allergen lists globally, although it may be equally important to acknowledge differences in priority allergens based on cultural and dietary preferences. It is incumbent upon health professionals to help their patients and clients identify foods to which they are allergic and aid in planning diets that are nutritionally adequate despite elimination of these foods.
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 | 0.000 | 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) | 0.002 | 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