Bibliographic record
Abstract
One in two women and one in five men suffer from osteoporotic fractures after the age of 50. Enabling children and young people to develop strong bones and achieve their maximum potential bone mass will help prevent undue bone loss and osteoporosis in later life. Although 70-80% of peak bone mass is genetically determined, the remainder is determined by dietary and environmental factors. The most important dietary factor for bone health is calcium, which in the UK is obtained mainly from dairy foods (45%) and cereal-based foods (27%). In the UK one-quarter of teenage girls consume insufficient calcium to meet their minimum dietary requirements. The majority of teenage boys and girls fail to meet the UK Government's targets for calcium intakes. This is an important public health issue as 90% of peak bone mass is attained by the age of approximately 18 years in girls and 20 years in boys. Health professionals need to be aware of the importance of childhood and adolescence for building healthy bones and to work with this age group to promote the dietary and lifestyle factors that contribute to bone health and peak bone mass. They could usefully include advice on including three helpings of calcium in the diet each day, as highlighted in the current "3-a-Day" campaign.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".