Using a Homeogram to Detect Sleep in Free-living Animals
Bibliographic record
Abstract
Abstract Sleep is appreciated as a behavior critical to homeostasis, performance, and fitness. Yet, most of what we know about sleep comes from humans or controlled laboratory experiments. Assessing sleep in wild animals is challenging, as it is often hidden from view, and electrophysiological recordings that define sleep states are difficult to obtain. Accelerometers have offered great insight regarding gross movement, although ambiguous quiescent states like sleep have been largely ignored, limiting our understanding of this ubiquitous behavior. We developed a broadly applicable sleep detection method called a homeogram that can be applied to accelerometer data collected from wild animals. We applied our methodology to detect sleep in free-ranging North American red squirrels ( Tamiasciurus hudsonicus ) in a region that experiences drastic seasonal shifts in light, temperature, and behavioral demands. Our method characterized sleep in a manner consistent with limited existing studies and expanded those observations to provide evidence that red squirrels apply unique sleep strategies to cope with changing environments. Applying our analytical strategy to accelerometer data from other species may open new possibilities to investigate sleep patterns for researchers studying wild animals.
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 | 0.000 |
| 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".