The Authors Reply
Bibliographic record
Abstract
We appreciate Dr. Grant's letter (1) regarding our article (2), in which we reported increased risk of several types of cancer among men working at night. Dr. Grant suggests that a likely biological explanation for our findings is that night workers have reduced exposure to outdoor daylight and thus lower vitamin D levels. This would assume that night workers had lower overall direct exposure to sunlight than day workers. In fact, most day workers work indoors. The vast majority of people, irrespective of whether they work during the day or at night, have an opportunity for sunlight exposure outside of work hours. In an earlier publication (3) based on the same data set as the one used in our article (2), we reported on cancer risk related to the frequency of engaging in sports/outdoor activities during leisure time throughout adult life. As compared with men who had never or not often engaged in sports/outdoor recreational activities, men who often did had lower risks of smoking-related cancers, such as lung, esophageal, and bladder cancers, and higher risks of melanoma. Only marginal decreases in the risks of prostate, rectal, and pancreatic cancers were apparent among men who often engaged in sports/outdoor activities. Whether the inverse associations could be attributable to sunlight exposure and/or to other factors correlated with sports/outdoor activities, such as physical activity or diet, remains to be established.
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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.134 | 0.094 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".