Chronotype variation drives night-time sentinel-like behaviour in hunter–gatherers
Bibliographic record
Abstract
Sleep is essential for survival, yet it also represents a time of extreme vulnerability to predation, hostile conspecifics and environmental dangers. To reduce the risks of sleeping, the sentinel hypothesis proposes that group-living animals share the task of vigilance during sleep, with some individuals sleeping while others are awake. To investigate sentinel-like behaviour in sleeping humans, we investigated activity patterns at night among Hadza hunter-gatherers of Tanzania. Using actigraphy, we discovered that all subjects were simultaneously scored as asleep for only 18 min in total over 20 days of observation, with a median of eight individuals awake throughout the night-time period; thus, one or more individuals was awake (or in light stages of sleep) during 99.8% of sampled epochs between when the first person went to sleep and the last person awoke. We show that this asynchrony in activity levels is produced by chronotype variation, and that chronotype covaries with age. Thus, asynchronous periods of wakefulness provide an opportunity for vigilance when sleeping in groups. We propose that throughout human evolution, sleeping groups composed of mixed age classes provided a form of vigilance. Chronotype variation and human sleep architecture (including nocturnal awakenings) in modern populations may therefore represent a legacy of natural selection acting in the past to reduce the dangers of sleep.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".