Bibliographic record
Abstract
How should animals sleep in groups? Because sleeping reduces the ability of an individual to detect potential threats, not all individuals should sleep at the same time. The obvious solution of taking turns to sleep is not documented in animal groups. Individuals can also organize their sleeping bouts independently of each other but this simple strategy can be dangerous if too many individuals happen to sleep at the same time. One solution to this problem is to monitor the behaviour of other group members and adjust sleeping bouts accordingly. For instance, as the number of sleeping individuals increases, companions may decide that it must be a safe time to sleep. However, when fewer group members are sleeping, an individual may benefit by curtailing sleep, given that it would be more vulnerable than vigilant group members should an attack occur. Such monitoring can therefore lead to contagious behaviour in the group, which can be detected in a group by collective waves of activities through time. Using spectral analysis, I investigated the proportion of sleeping birds in loafing gulls (Larus spp.) as a function of time over 2 yr and found that in many groups, the proportion of sleeping birds rises and decreases in a systematic and statistically significant fashion. These results add more weight to the now increasingly supported view that vigilance in general is a social phenomenon and suggest that adaptive behaviour at the level of the individual can lead to collective phenomena such as waves of sleep in animal groups.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".