Epidemiology of skin changes in endangered Southern Resident killer whales (Orcinus orca)
Bibliographic record
Abstract
Photographic identification catalogs of individual killer whales (Orcinus orca) over time provide a tool for remote health assessment. We retrospectively examined digital photographs of Southern Resident killer whales in the Salish Sea to characterize skin changes and to determine if they could be an indicator of individual, pod, or population health. Using photographs collected from 2004 through 2016 from 18,697 individual whale sightings, we identified six lesions (cephalopod, erosions, gray patches, gray targets, orange on gray, and pinpoint black discoloration). Of 141 whales that were alive at some point during the study, 99% had photographic evidence of skin lesions. Using a multivariate model including age, sex, pod, and matriline across time, the point prevalence of the two most prevalent lesions, gray patches and gray targets, varied between pods and between years and showed small differences between stage classes. Despite minor differences, we document a strong increase in point prevalence of both lesion types in all three pods from 2004 through 2016. The health significance of this is not clear, but the possible relationship between these lesions and decreasing body condition and immunocompetence in an endangered, non-recovering population is a concern. Understanding the etiology and pathogenesis of these lesions is important to better understand the health significance of these skin changes that are increasing in prevalence.
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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 | 0.001 |
| 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) | 0.001 | 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".