Bibliographic record
Abstract
Sleep remains a significantly understudied behaviour among non-human primates, with quantitative data available for only about ten percent of species. This research addresses key gaps in the literature by contributing novel observational sleep data for several ape species, including chimpanzees (Pan troglodytes), bonobos (Pan paniscus), gorillas (Gorilla gorilla gorilla), gibbons (Hylobates moloch, H. pileatus), and orangutans (Pongo pygmaeus, P. abelii), across 450 cumulative nights of videographic observation. These data expand the Primate Sleep Database and offer critical insights into sleep architecture—total sleep time, sleep fragmentation, sleep efficiency, and proportions of REM sleep—across large bodied and small-bodied non-human apes. Species comparisons revealed that African great apes exhibit less sleep fragmentation and higher proportions of REM sleep than their Asian counterparts, while small-bodied apes demonstrated longer total sleep time but lower sleep efficiency. Within species, social factors appear to influence sleep: bonobos, known for their tolerant social dynamics, slept longer than chimpanzees, and in gorillas, dominant individuals exhibited lower sleep fragmentation than subordinates. Across all species studied, males showed shorter total sleep time, greater fragmentation, lower efficiency, and less REM sleep than females, suggesting sexual selection pressure may influence sleep patterns. This research lays the groundwork for broader evolutionary analyses and provides a foundation for understanding the socio-ecological drivers of sleep variation within the primate order.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".