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\nknowledge about species ecology. In rare instances where Arctic cetaceans can be spotted,\ninformation about their population structure and biology can be gleaned through observation;\nhowever, direct observations are difficult in high ice cover. Ice entrapments, where cetaceans are\ncrowded under increasing ice cover until escape or drowning, have given insight into cetacean\npopulations since the 18th century, and today new genetic analyses can allow us to reexamine the\npopulation structure of these Arctic species and add to previous research on ice entrapments and\nnarwhal social structure. In this thesis’ second chapter, I first review 138 cetacean ice entrapment\noccurrences globally and show that ice entrapments are a significant source of mortality for\ncetaceans, killing more than 18,500 individuals in 13 different species since 1900. In the third\nchapter, I use population genetics to study the social structure of the Canadian Arctic narwhal\n(Monodon monoceros) from a 2008 ice entrapment. Through pair-wise relatedness and cluster\nanalysis, I determined that within an ice-entrapped herd (n=245), there were 8 genetically related\nclusters with an average size of 30.6, indicating that the species may follow a fission-fusion\nsocial structure like other smaller, social cetaceans. This work may contribute to species\nmanagement decisions and be valuable for emergency management of ice entrapments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".