Extreme physiological adaptations as predictors of climate‐change sensitivity in the narwhal,<i>Monodon monoceros</i>
Bibliographic record
Abstract
Abstract Rapid changes in sea ice cover associated with global warming are poised to have marked impacts on polar marine mammals. Here we examine skeletal muscle characteristics supporting swimming and diving in one polar species, the narwhal, and use these attributes to further document this cetacean's vulnerability to unpredictable sea ice conditions and changing ecosystems. We found that extreme morphological and physiological adaptations enabling year‐round Arctic residency by narwhals limit behavioral flexibility for responding to alternations in sea ice. In contrast to the greyhound‐like muscle profile of acrobatic odontocetes, thelongissimus dorsiof narwhals is comprised of 86.8%± 7.7% slow twitch oxidative fibers, resembling the endurance morph of human marathoners. Myoglobin content, 7.87 ± 1.72 g/100 g wet muscle, is one of the highest levels measured for marine mammals. Calculated maximum aerobic swimming distance between breathing holes in ice is <1,450 m, which permits routine use of only 2.6%–10.4% of ice‐packed foraging grounds in Baffin Bay. These first measurements of narwhal exercise physiology reveal extreme specialization of skeletal muscles for moving in a challenging ecological niche. This study also demonstrates the power of using basic physiological attributes to predict species vulnerabilities to environmental perturbation before critical population disturbance occurs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".