Disability, Compensatory Behavior, and Innovation in Free‐Ranging Adult Female <scp>J</scp>apanese Macaques (<i><scp>M</scp>acaca Fuscata</i>)
Bibliographic record
Abstract
Little is known about consequences of disability in nonhuman primates, yet individuals with disabilities can reveal much about behavioral flexibility, innovation, and the capabilities of a species. The Macaca fuscata population surrounding the Awajishima Monkey Center has experienced high rates of congenital limb malformation for at least 40 years, creating a unique opportunity to examine consequences of physical impairment in situ, in a relatively large sample of free-ranging adult monkeys. Here we present behavioral data on 11 disabled adult females and 12 nondisabled controls from 279 hours of randomly ordered 30-minute focal animal follows collected during May-August in 2005, 2006, and 2007. We quantified numerous statistically significant disability-related behavioral differences among females. Disabled females spent less time begging for peanuts from tourists, and employed a behavioral variant of such peanut begging; they had a lower frequency of hand use in grooming and compensated with increased direct use of the mouth or a two-arm pinch technique; and they had a higher frequency of self-scratching, and more use of feet in self-scratching. Self-scratching against substrates was almost exclusively a disability associated behavior. Two females used habitual bipedalism. These differences not withstanding, disabled females behaved similarly to controls in many respects: overall reliance on provisioned and wild foods, time spent feeding, and feeding efficiency did not differ among females, and there was no time difference in behavior performed arboreally or terrestrially. Disabled adult females were able to compensate behaviorally to perform social and life-sustaining activities, modifying existing behaviors to suit their individual physical situations and, occasionally, inventing new ways of doing things.
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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 | 0.000 |
| 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".