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Extreme physiological adaptations as predictors of climate‐change sensitivity in the narwhal,<i>Monodon monoceros</i>

2010· article· en· W1992103543 on OpenAlexfundno aff
Terrie M. Williams, Shawn R. Noren

Bibliographic record

VenueMarine Mammal Science · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNational Marine Fisheries ServiceOffice of Naval ResearchU.S. Fish and Wildlife ServiceTransport Canada
KeywordsUrsus maritimusBiologyForagingSea iceMarine mammalPopulationBelugaEcologyArcticZoologyOceanographyGeology

Abstract

fetched live from OpenAlex

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, the longissimus dorsi of 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 &lt;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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.045
GPT teacher head0.255
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations57
Published2010
Admission routes1
Has abstractyes

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