Shape variation in the eye patch and dorsal fin of southern hemisphere killer whales (Orcinus orca)
Bibliographic record
Abstract
Abstract Morphological variation within and among species plays a critical role in evolutionary processes, influencing adaptation, survival, and reproductive success. Killer whale ( Orcinus orca ) morphology is known to vary on both an individual and population level with several ecotypes or forms documented worldwide. However, the extent of morphological variation among killer whales in Australian waters remains unclear, both among individuals within the region and in comparison to those in other parts of the southern hemisphere. This study assessed eye patch and dorsal fin shape variation in Australian and Antarctic killer whales to explore the evolutionary relationships among these groups. A large dataset of imagery was compiled and processed to achieve this, which provided representative sample sizes for five separate study groups: northwest Australia (NW), southwest Australia (SW), southeast Australia (SE), Antarctic type A (AA) and Antarctic type B1 (AB). Elliptical Fourier analysis was used to extract the feature outlines and enable multivariate data analyses. Principal component analysis and pairwise comparisons revealed significant morphological differences both within and between Australian and Antarctic killer whales. Eye patch shape variation was driven by the degree of taper and overall width whereas dorsal fin shape variation was driven by falcateness and broadness at its base. Hierarchical cluster analysis revealed considerable variation amongst these features, while linear discriminate analysis indicated that individuals could not be reliably classified into their respective study groups based on eye patch and dorsal fin shape alone. Nonetheless, these findings suggest the presence of both a tropical and temperate form of killer whale in Australia, with the latter resembling both the Antarctic Type A and B2s morphologically. To better understand their connectivity and divergence, dedicated research is needed to assess the evolutionary history of these populations. Such knowledge will be vital in defining global conservation management units for killer whales which are still considered a single, data deficient species by the International Union for Conservation of Nature (IUCN).
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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 | 0.001 | 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".