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BACULAR SIZE, GROWTH, AND ALLOMETRY IN THE LARGEST EXTANT OTARIID, THE STELLER SEA LION(<i>EUMETOPIAS JUBATUS</i>)

2000· article· en· W2180144950 on OpenAlexaff
Edward H. Miller, Kenneth W. Pitcher, Thomas R. Loughlin

Bibliographic record

VenueJournal of Mammalogy · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAllometryBiologySea lionExtant taxonMatingIsometric exerciseRostrumZoologyEcologyAnatomyEvolutionary biologyPhysiology

Abstract

fetched live from OpenAlex

Bacula are relatively small in terrestrially mating species of pinnipeds (otariids and elephant seals, Mirounga), perhaps reflecting adaptive size reduction to minimize bacular fracture. Fur seals and sea lions (Otariidae) are a good group with which to investigate this question, because most species copulate solely on land and body size varies interspecifically. We studied bacular size and relative growth in the largest extant otariid, the Steller sea lion (Eumetopias jubatus). Bacula roughly tripled in length and increased 30-fold in mass between 1 and 8 years of age. Allometric relationships changed over development; bacular length and mass changed from being initially positively allometric to body length to negatively allometric and isometric, respectively; bacular mass and thickness were positively allometric to body length throughout life, and apical growth was isometric then was positively allometric to bacular length. In adults (>7 years of age), bacula averaged 18.1 cm length (6.2% of body length), 36.7 g mass, and 2.02 g/cm density (mass : length). The baculum of Eumetopias is about the same length relative to body length as in other adult male otariids but is about twice the density, presumably to increase strength. Information on small or aquatically mating species of otariid are needed to extend our findings and interpretations.

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 score0.208
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.008
GPT teacher head0.212
Teacher spread0.203 · 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

Citations26
Published2000
Admission routes1
Has abstractyes

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