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Record W2063517987 · doi:10.1139/z08-144

Optimal fineness ratio for minimum drag in large whales

2009· article· en· W2063517987 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Zoology · 2009
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFinenessDragDrag coefficientMass ratioDiameter ratioMechanicsBiologyMathematicsPhysicsMaterials scienceComposite materialAstrophysics

Abstract

fetched live from OpenAlex

The optimum fineness ratio (X = L/d, where L and d are body length and profile height, respectively) for minimum drag is about 4.5 and many fast swimming fish are characterized by values of this order. However, values for large whales that undergo extensive migrations (e.g., Balaenopteridae, Balaenidae, and Physeteridae) are as high as 8. A plot of fineness ratio versus mass (M) for different species of large whales shows that the optimal fineness ratio for minimum drag and therefore the minimum cost of transport increases slowly with increasing mass (X = 4M 0.06 ). Optimal fineness ratio was determined from a simple hydromechanical model based on the sum of friction and pressure drag on an equivalent cylindrical body, which indicate a small positive dependence (0.11) of optimal fineness ratio for minimum drag with increasing body mass, suggesting an adaptation for reducing the energy cost of swimming.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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