Bibliographic record
Abstract
PURPOSE OF REVIEW: Biannual clock changes to and from daylight saving time have been pervasive in many societies for over 50 years. Governments are considering abandoning this practice and choosing a single permanent time. RECENT FINDINGS: Our endogenous circadian clock follows our photoperiod, which changes over the year. The acute disruption caused by changing our clocks can affect safety (motor vehicle and on the job accidents), health (cardiovascular disease, drug overdoses, suicide), and human behavior (sport performance, generosity, and procrastination). Although abandoning the clock change could help avoid these acute harms, choosing the wrong permanent time could lead to chronic circadian misalignment, which could have even more profound implications for health, safety, and human behavior. SUMMARY: Ceasing the biannual clock change may be a good choice, but governments need to be mindful of which permanent time to adopt. Many regions of the world already follow the wrong time during standard time, and circadian misalignment would be amplified by moving to permanent daylight saving time. In many regions, Standard Time better aligns with our circadian clock, thus providing a more natural light cycle that minimizes circadian misalignment.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".