Testing parallel laser image scaling for remotely measuring body dimensions on mantled howling monkeys (<i>Alouatta palliata</i>)
Bibliographic record
Abstract
Body size is a fundamental variable for many studies in primate biology. However, obtaining body dimensions of wild primates through live capture is difficult and costly, so developing an alternative inexpensive and non-invasive method is crucial. Parallel laser image scaling for remotely measuring body size has been used with some success in marine and terrestrial animals, but only one arboreal primate. We further tested the efficacy of this method on the arboreal mantled howling monkey (Alouatta palliata) in La Pacifica, Costa Rica. We calculated interobserver error, as well as the method's repeatability when measuring the same animal on different occasions. We also compared measurements obtained physically through live capture with measurements obtained remotely using parallel laser image scaling. Our results show that the different types of error for the remote technique are minimal and comparable with the error rates observed in physical methods, with the exception of some dimensions that vary depending on the animals' posture. We conclude that parallel laser image scaling can be used to remotely obtain body dimensions if careful consideration is given to factors such as species-specific morphology and postural habits.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".