Computed Tomography of the Mandibles of a Stranded Offshore Killer Whale (Orcinus orca)
Bibliographic record
Abstract
A mature, adult female, offshore killer whale (Orcinus orca) was stranded deceased in Portage Bay, Alaska, in October 2015. Full necropsy examination with histopathology was performed. Consistent with previous studies of offshore killer whales, and thought to be a result of their unique elasmobranch diet, all the teeth were significantly abraded and almost flush with the gingival margin. Age was estimated at 30-35 years based on annuli and growth arrest lines in a remaining tooth. The dentate portion of the mandibles were excised en bloc and frozen until imaging could be completed. Radiography and computed tomography revealed lesions consistent with severe abrasion, pulp exposure and evidence of endodontic and/or periodontal disease in nine of the 15 mandibular teeth present (60.0%). Only five (33.3%) teeth were suspected to have been vital at the time of death based on imaging. Lesions were more severe rostrally, with the caudal teeth less affected. Autolysis precluded gingival histopathology and no teeth were analyzed histologically. Necropsy examination revealed a likely multifactorial cause of death, with most significant lesions including the severe chronic periodontal/endodontic disease with abrasion, inanition and emaciation with possible cardiovascular disease. This case highlights the importance of imaging in evaluating periodontal and endodontic status, especially post mortem when other tissues are no longer available, and demonstrates that periodontal and endodontic disease occur naturally in this species and can be a significant cause of morbidity in mature free-ranging killer whales of the offshore ecotype.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".