Parallel‐laser photogrammetry to estimate body size in free‐ranging mammals
Bibliographic record
Abstract
ABSTRACT It is not always practical to hand‐measure body size of free‐ranging animals. In recent years, parallel‐laser photogrammetry has become increasingly common for obtaining remote estimates of body size. However, it is unknown how well this technique might capture variation in body size of curvilinear features or whether the distance between parallel‐laser calipers is altered when projected onto a curved surface. We describe a photogrammetric system that may be useful for obtaining body‐size measurements from unrestrained large mammals that permit approach. We tested the use of parallel‐laser photogrammetry to estimate the size of curvilinear features in domestic horses ( Equus ferus caballus ) and identified morphometrics that explained variation in body weight. Despite projecting the lasers onto a curved surface (the barrel of a horse), we achieved accurate photogrammetric estimates of linear hand‐measurements. The curvilinear hand‐measurements also showed strong correlations ( R 2 ≥ 0.996) with their respective linear photogrammetric estimates, and most photogrammetric estimates had high reliability. Using 3 variables of body size, photogrammetric estimates and hand‐measurements explained 86.0% and 96.2% of the variation in weight, respectively. © 2015 The Wildlife Society.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".