MétaCan
Menu
Back to cohort
Record W4200041582 · doi:10.1111/mms.12898

Variation in the endogenous intact waxes of odontocetes: There is more than one way to build an acoustic receiver

2021· article· en· W4200041582 on OpenAlexaff
Ana Michael, Suzanne M. Budge, Andrew J. Westgate, Hillary L. Glandon, Heather N. Koopman

Bibliographic record

VenueMarine Mammal Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWaxWax esterFatty acidEndogenyBiologyBiochemistryChemistry

Abstract

fetched live from OpenAlex

Abstract Odontocetes possess specialized fat bodies in and around the mandibles for sound reception which have complex topographical distributions of unique endogenous lipids (triacylglycerols and wax esters [WE]). Although there is diversity across species in the fatty acid (FA) and fatty alcohol (FAlc) components of WE, little is understood about the composition and placement of the intact molecules, which will likely impact acoustic function. We aimed to determine the composition and distribution of intact waxes in the jaw fats from five species representative of three odontocete families: delphinids, kogiids, and ziphiids. Total lipid content was similar in all groups, but the WE content of that lipid (21.3%–53.3% of total lipid) and the identity of intact WE molecules showed a high degree of variation, especially in the short‐chain fatty acid components. In contrast, the FAlc elements were surprisingly well conserved. There were 26 intact WE that were common to all species but the delphinids had 12 additional WE with short‐chain fatty acids ( i ‐5:0 specifically) not found in the other animals examined here. Our study suggests that this highly specialized tissue has evolved several different biochemical pathways, and that there may be multiple strategies for building acoustic fat bodies.

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 score0.197
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.252
Teacher spread0.221 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueMarine Mammal ScienceSame topicMarine animal studies overviewFrench-language works237,207