Bibliographic record
Abstract
Emerging scientific evidence strongly supports the beneficial role of vitamin D in reducing the risk of incidence and death from many chronic and infectious diseases. This study estimates increases in melanoma and nonmelanoma skin cancer mortality rates and decreases in chronic and infectious disease mortality rates in the US from the standpoint of approximately doubling population doses of solar UVB to increase mean serum 25-hydroxyvitamin D levels from 16 ng/mL for black Americans and 25 ng/mL for white Americans to 45 ng/mL. The primary benefits are expected to come from reductions in cancer and cardiovascular diseases. Although a few thousand excess deaths per year might occur from melanoma and skin cancer, the avoided premature death rate could be near 400,000/ year, with most of the avoided deaths coming late in life. While oral sources of vitamin D could be used instead of UVB or when UVB irradiance is not available, public health policies do not yet recommend the 3,000-4,000 IU/day required to raise serum 25-hydroxyvitamin D levels to the levels required for optimal health, which would be required before vitamin D fortification levels in food can be raised. Until then, moderate solar UVB irradiance remains an import source, and the health benefits greatly outweigh the risks.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.024 |
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".