Measuring physical traits of primates remotely: the use of parallel lasers
Bibliographic record
Abstract
Physical traits, such as body size, and processes like growth can be used as indices of primate health and can add to our understanding of life history and behavior. Accurately measuring physical traits in the wild can be challenging because capture is difficult, disrupts animals, and may cause injury. To measure physical traits of arboreal primates remotely, we adapted a parallel laser technique that has been used with terrestrial and marine mammals. Two parallel lasers separated by a known distance (4 cm) and mounted onto a digital camera are projected onto an animal. When a photograph is taken, the laser projections on the target provide a scale bar. We validated the technique for measuring the physical traits of identifiable red colobus monkeys (Procolobus rufomitratus) in Kibale National Park, Uganda. First, we photographed the tails of monkeys with laser projections and compared these with measurements previously obtained when the animals were captured. Second, we manually measured the distance between two markers placed on tree branches at similar heights to those used by monkeys, and compared them with the measurements obtained through digital photographs of the markers with parallel laser projections. The mean tail length of the monkeys via manual measurements was 63.3+/-4.4 cm, and via remote measurements was 63.0+/-4.1 cm. The mean distance between the markers on tree branches via manual measurements was 13.8+/-3.59 cm, and via remote measurements was 13.9+/-3.58 cm. The mean error using parallel lasers was 1.7% in both cases. Although the needed precision will depend on the question asked, our results suggest that sufficiently precise measurements of physical traits or substrates of arboreal primates can be obtained remotely using parallel lasers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".