Sleeping sites, sleeping trees, and sleep‐related behaviors of black crested gibbons (<i>Nomascus concolor jingdongensis</i>) at Mt. Wuliang, Central Yunnan, China
Bibliographic record
Abstract
Data on sleep-related behaviors were collected for a group of central Yunnan black crested gibbons (Nomascus concolor jingdongensis) at Mt. Wuliang, Yunnan, China from March 2005 to April 2006. Members of the group usually formed four sleeping units (adult male and juvenile, adult female with one semi-dependent black infant, adult female with one dependent yellow infant, and subadult male) spread over different sleeping trees. Individuals or units preferred specific areas to sleep; all sleeping sites were situated in primary forest, mostly (77%) between 2,200 and 2,400 m in elevation. They tended to sleep in the tallest and thickest trees with large crowns on steep slopes and near important food patches. Factors influencing sleeping site selection were (1) tree characteristics, (2) accessibility, and (3) easy escape. Few sleeping trees were used repeatedly by the same or other members of the group. The gibbons entered the sleeping trees on average 128 min before sunset and left the sleeping trees on average 33 min after sunrise. The lag between the first and last individual entering the trees was on average 17.8 min. We suggest that sleep-related behaviors are primarily adaptations to minimize the risk of being detected by predators. Sleeping trees may be chosen to make approach and attack difficult for the predator, and to provide an easy escape route in the dark. In response to cold temperatures in a higher habitat, gibbons usually sit and huddle together during the night, and in the cold season they tend to sleep on ferns and/or orchids.
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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 | 0.001 | 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".