Sound reasons for unsound sleep: Comparative support for the sentinel hypothesis in industrial and nonindustrial groups
Bibliographic record
Abstract
Background and objectives: Sleep is a vulnerable state in which individuals are more susceptible to threat, which may have led to evolved mechanisms for increasing safety. The sentinel hypothesis proposes that brief awakenings during sleep may be a strategy for detecting and responding to environmental threats. Observations of sleep segmentation and group sentinelization in hunter-gatherer and small-scale communities support this hypothesis, but to date it has not been tested in comparisons with industrial populations characterized by more secure sleep environments. Methodology: = 347) Americans. We compared nighttime awakenings between these groups using actigraphically-measured sleep data. We fit linear models to assess whether WASO varies across groups, controlling for sex and age. Results: We found that WASO varies significantly by group membership and is highest in Hadza (2.44 h) and Malagasy (1.93 h) and lowest in non-Hispanic Whites (0.69 h). Hispanics demonstrate intermediate WASO (0.86 h), which is significantly more than NHW participants. After performing supplementary analysis within the Hispanic sample, we found that WASO is significantly and positively associated with increased perception of neighborhood violence. Conclusions and implications: Consistent with principles central to evolutionary medicine, we propose that evolved mechanisms to increase vigilance during sleep may now be mismatched with relatively safer environments, and in part responsible for driving poor sleep health.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".