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Record W2113101347 · doi:10.1111/mms.12230

Estimating maximal force output of cetaceans using axial locomotor muscle morphology

2015· article· en· W2113101347 on OpenAlexaff
Logan H. Arthur, William A. McLellan, Marina A. Piscitelli, Sentiel A. Rommel, Becky L. Woodward, Jeremy P. Winn, Charles W. Potter, D. Ann Pabst

Bibliographic record

VenueMarine Mammal Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
FundersUniversity of North Carolina WilmingtonNational Park ServiceNational Oceanic and Atmospheric AdministrationNorth Carolina State University
KeywordsThrustWhaleBiologyDragBottlenose dolphinCetaceaRange (aeronautics)AnatomyFisheryPhysicsMechanicsMaterials science

Abstract

fetched live from OpenAlex

Abstract Cetaceans span a large range of body sizes and include species with the largest known locomotor muscles. To date, force output and thrust production have only been directly measured in the common bottlenose dolphin ( Tursiops truncatus ), although thrust forces have been hydrodynamically modeled for some whales. In this study, two metrics of epaxial muscle size—cross‐sectional area ( CSA ) and mass—were used to estimate force output for 22 species ( n = 83 specimens) ranging in size from bottlenose dolphins to blue whales ( Balaenoptera musculus ). Relative to total body length ( TL ), maximum force output estimated based upon both muscle CSA ( TL 1.56 ± 0.05 ) and mass ( TL 2.64 ± 0.07 ) scaled at rates lower than those predicted by geometric scaling, suggesting relative force output decreases with increasing body size in cetaceans. Estimated maximal force outputs were compared to both published drag forces and to the breaking strengths of commercial fishing lines known to entangle whales. The breaking strengths of these lines are within the same order of magnitude, and in some cases, exceed the estimated maximal force output of whales. These results suggest that while powerful animals, large whales may be unable to break the extremely strong fishing line used today.

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)0.001
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 score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.004
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 head0.043
GPT teacher head0.271
Teacher spread0.227 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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