Alopecia Scoring: The Quantitative Assessment of Hair Loss in Captive Macaques
Bibliographic record
Abstract
Many captive animals show forms of pelage loss that are absent in wild or free-living conspecifics, which result from grooming or plucking behaviours directed at themselves or at other individuals. For instance, dorsal hair loss in primates such as rhesus macaques (Macaca mulatta) in research facilities, results from excessive hair-pulling or over-grooming by cage-mates. This behaviour appears to be associated with stress, and is controllable to some extent with environmental enrichment. Quantifying alopecia in primates (as in many species) is therefore potentially useful for welfare assessment. A simple system for scoring alopecia was developed and its reliability was tested. Study 1 showed high interobserver reliability between two independent scorers in assessing the state of monkeys coats from photographs. Study 2 showed that there were no significant differences between the scores derived from photographs and from direct observations. Thus, where hair loss due to hair pulling exists in captive primates, this scoring system provides an easy, rapid, and validated quantitative method, for use in assessing the success of attempts to reduce it via improved husbandry. In the future, such scoring systems might also prove useful for quantifying barbering in laboratory rodents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".