Bibliographic record
Abstract
Sources of dietary calcium vary worldwide. Dairy is included in European, Middle Eastern, and South Asian cuisines; however, it is not a part of traditional Polynesian cuisines, nonpastoral African cuisines, the cuisines of the indigenous peoples of the Americas, or most Asian cuisines. Westernization has resulted in increased dairy intake among many ethnic groups that did not historically consume it. Although dairy is often the top source of calcium where it is consumed, it does not always provide the majority of calcium in the diet and other sources of calcium are required for adequacy. Nondairy contributors of calcium in Asia include grains, vegetables, legumes, and fish with edible bones. Soups made with vinegar-soaked bones and preserved eggs may be important to the calcium nutrition of postpartum women. In Africa, wild greens and insects contribute calcium to the diet. In some Latin American countries, tortillas prepared using flour from corn kernels soaked in calcium hydroxide contribute to calcium nutrition. To widen our understanding of calcium nutrition increased knowledge of the calcium contribution of nondairy food sources like insects, wild greens and Asian soups is required.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".