Inferring sperm whale ( <i>Physeter macrocephalus</i> ) sex and developmental stage using aerial photogrammetry
Bibliographic record
Abstract
Demographic data (i.e. sex and age) are fundamental for analyzing behaviour patterns and evaluating the reproductive potential of a population. However, determining these traits in the wild can be challenging, particularly for marine animals with concealed genitals that spend most of their time underwater. Here, we developed a minimally invasive method to infer the developmental stage and sex of sperm whales (Physeter macrocephalus) off the Galápagos Islands (N = 51) using uncrewed aerial vehicle (UAV) photogrammetry. We leveraged historic whaling data on sperm whale growth and sexual dimorphism to assign developmental stages to individuals based on their body lengths. We applied Bayesian theory to estimate the probability that individuals were female based on their morphometry. Our methods allowed confident classification of the developmental stage and sex for most individuals. Moreover, an examination of the inferred developmental stage and sex of individuals participating in peduncle diving revealed patterns congruent with previous findings, which show that this behaviour is predominantly directed at females and performed by subadult individuals. Our method offers an efficient, low-cost means of obtaining demographic information from live sperm whales, contributing to a deeper understanding of their behavioural development and informing population status and viability assessments.
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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".