Bowhead whale mortality event in Nunavut, Canada – Autumn, 2020
Bibliographic record
Abstract
Cetacean mortality events in the Arctic often go underreported compared with events in more highly populated regions. Here, we report a mortality event involving the death of 11 bowhead whales around the Gulf of Boothia, Canada. The whales were discovered between October 2020–April 2021. Reports of 11 dead bowhead whales within six months in one area raised concerns among local hunters and community members. Due to the remoteness of these strandings and challenges with access, complete necropsies were not performed, but local Inuit collected tissue samples from eight of the whales. Possible reasons for these deaths include unusual weather events, nutritional stress/starvation, metabolic abnormalities, infectious disease, anthropogenic impacts (such as vessel collisions) and killer whale predation. To determine the most likely cause of these strandings, demographic, temporal, environmental, epidemiological, pathologic and contaminant analyses were performed. Results were compared with published accounts and historical data where appropriate. Killer whale sightings by local Inuit both before and during the stranding events confirmed the presence of these predators in close proximity to the carcass locations, with predation marks observed in several carcasses. We conclude that this was the most probable direct contributing factor to the mortality event. Indirect contributing factors might also include reduced ice coverage as a result of climate change and nutritional stress. Further monitoring of this population is required to assess health from both a scientific and an Indigenous perspective.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".