Rank and social context influence sleep in wild chimpanzees
Bibliographic record
Abstract
Sleep is subject to Darwinian fitness and thereby constrained by ecological and social factors. Nevertheless, most comparative research on sleep is conducted in laboratory settings, detached from environmental and social influences, which is problematic for evolutionary theories. We examined the natural sleeping patterns of wild chimpanzees in Budongo Forest, Uganda, using a remote-controlled, infrared camera system. We found that sleep in chimpanzees was significantly affected by social factors, including the sleeper's own rank and the composition of the nearby sleeping party. Nesting in groups increased sleep duration and decreased sleep fragmentation compared with sleeping alone, despite the fact that it delayed nesting times and advanced wake times. Rank had little impact on female sleep but a strong influence on male sleep, with high-ranking males generally experiencing shorter and more fragmented sleep compared with subordinate males. The presence of sexually active females also reduced sleep duration, by delaying nest building, advancing wake time, and increasing sleep fragmentation. Our data show that natural sleep patterns in chimpanzees are largely determined by social variables that continue to exert their influence into the night. We discuss the implications of studying sleep patterns of our closest relatives in ecologically and socially valid situations for future research on the evolution 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 | 0.001 |
| 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 | 0.001 |
| 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".