Using laser photogrammetry to measure long-finned pilot whales (Globicephala melas)
Bibliographic record
Abstract
Knowledge of animal morphometry is important to understanding their ecology. By attaching two parallel lasers to a camera, known as laser photogrammetry (LP), a scale is projected onto photographed animals, allowing measurement of their body. Our primary aims were to test LP precision, and to estimate body length from dorsal-fin dimensions of Globicephala melas. Secondary aims involved demonstrating applications of LP, such as sex and leader determination. Using photographs taken over two-months, we measured dorsal base lengths (DBL) of 194 individuals individually-identified with natural markings. Results indicated 33 individuals were photographed in multiple encounters and eight matched previously-sexed whales. A mean difference of <2.1% between DBL’s of 58% of repeatedly-sighted individuals was found, and whales closer to the boat (<22m) produced more precise measures. The length from the blowhole to anterior insertion of the dorsal fin (BAID) was a better predictor of total body length in stranded whales than DBL, and laser-estimated lengths fell almost all within known pilot whale size. Despite our small sample size, we showed two examples of how LP could be applied in research: (1) males and females had similar DBL (n=8), but large males could be distinguished using DBL; (2) leaders were not necessarily bigger than other individuals in the same cluster (n=4). The ease of use of LP makes it a valuable tool in collecting measurements of body features, especially when coupled with photo-identification. Keywords: laser photogrammetry, morphometrics, measurement, length, Globicephala melas
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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 | 0.001 |
| 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".