Worldwide variation in shape and size of orca (<i>Orcinus orca</i>) saddle patches
Bibliographic record
Abstract
Abstract The global distribution of Orcinus orca (orcas/killer whales) encompasses populations which differ from each other. Saddle patch shapes and sizes were compared for nearly 4,000 individuals, in 48 geographically or ecologically divided groups/populations/ecotypes (GP/E), in four Ocean Basins. Some Antarctic GP/E had five shapes, contrary to previous studies, which found only one shape in these Southern GP/E. Pacific Resident ecotypes had the highest variation in saddle patch shapes. Globally, the most common shape was the ‘Smooth’ category. Saddle patch sizes were measured using a ratio of the width of the saddle patch compared to the width of the dorsal fin base and averaged within each GP/E. The narrowest saddle patches were observed in New Zealand waters. The widest saddle patches were observed at the Crozet Islands and the Falkland Islands (Islas Malvinas). Globally, we found that the shape and size of saddle patches helped to define various GP/E, reinforcing earlier predictions that this pigmentation may be indicative of population divisions. Our findings may help with describing poorly defined or undescribed ecotypes. Such results may therefore aid assessments by management authorities/policy makers and provide levels of guidance in the creation of conservation or recovery plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".