Estimating body mass of sperm whales from aerial photographs
Bibliographic record
Abstract
Abstract Body mass is a fundamental feature of animal physiology. Although sperm whales ( Physeter macrocephalus ) are the largest toothed predators on earth, body mass is seldom included in studies of their ecophysiology and bioenergetics due to the inherent difficulties of obtaining direct measurements. We used UAV‐photogrammetry to estimate the weight of free‐ranging sperm whales. Aerial photographs (23 calves, 11 juveniles, 55 nonmother adults, 13 mothers) were collected in the Eastern Caribbean and Mediterranean Sea during 2017–2020. Body length, widths, and heights (dorso‐ventral distance at 5% increments) were measured from dorsal and lateral photographs, while body volume was calculated using an elliptical model. Volume varied noticeably (12.01 ± 4.79 m 3 ) in larger animals (>8 m), indicating fluctuations in body condition of adults and mothers. Volume was converted to mass, using tissue‐density estimates from catch data, animal‐borne tags, and body‐tissue composition. Average total body density ranged from 834 to 1,003 kg/m 3 , while the weight predictions matched with existing measurements and weight‐length relationships. Our body‐mass models can be used to study sperm whale bioenergetics, including inter‐ and intraseasonal variations in body condition, somatic growth, metabolic rates, and cost of reproduction.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".