Arctic whale mortality: understanding modern population losses for the future
Bibliographic record
Abstract
The remote, ice-covered habitat and reclusive nature of Arctic cetaceans have led to a gap in knowledge about species ecology. In rare instances where Arctic cetaceans can be spotted, information about their population structure and biology can be gleaned through observation; however, direct observations are difficult in high ice cover. Ice entrapments, where cetaceans are crowded under increasing ice cover until escape or drowning, have given insight into cetacean populations since the 18th century, and today new genetic analyses can allow us to reexamine the population structure of these Arctic species and add to previous research on ice entrapments and narwhal social structure. In this thesis’ second chapter, I first review 138 cetacean ice entrapment occurrences globally and show that ice entrapments are a significant source of mortality for cetaceans, killing more than 18,500 individuals in 13 different species since 1900. In the third chapter, I use population genetics to study the social structure of the Canadian Arctic narwhal (Monodon monoceros) from a 2008 ice entrapment. Through pair-wise relatedness and cluster analysis, I determined that within an ice-entrapped herd (n=245), there were 8 genetically related clusters with an average size of 30.6, indicating that the species may follow a fission-fusion social structure like other smaller, social cetaceans. This work may contribute to species management decisions and be valuable for emergency management of ice entrapments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".