MétaCan
Menu
Back to cohort
Record W7043981657

Variability In Fatty Acid Composition Of Bottlenose Dolphin (Tursiops Truncatus) Blubber As A Function Of Body Site, Season, And Reproductive State

2004· article· en· W7043981657 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of International Crisis and Risk Communication Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsBlubberFatty acidComposition (language)Bottlenose dolphinCetacea
DOInot available

Abstract

fetched live from OpenAlex

Odontocete blubber has been shown to be variable in composition and can be separated into strata visually, histologically, and biochemically. The purpose of this study was to examine fatty acid composition of bottlenose dolphin (Tursiops truncatus (Montagu, 1821)) blubber, and determine if differences exist between body sites, reproductive states, and (or) seasons. The influence of these variables on blubber composition could aid in the creation of a model that would use fatty acid signature analysis to evaluate diet in free-ranging populations. Blubber samples were obtained from freshly dead animals along the Texas and Louisiana coastlines. Samples from nine body sites were analyzed to investigate site variability, and from one site to evaluate differences due to season, reproductive state, and blubber layer. All body sites of animals sampled in the winter were statistically indistinguishable, indicating that biopsy samples could be obtained from any location on the animal for fatty acid analysis during this season; however, three distinct blubber layers were identifiable, and reproductive states were significantly different in terms of fatty acid composition. Seasonal differences in fatty acid composition were also highly significant for all one-site inner blubber layer samples. Ultimately, the differences in fatty acid composition could have resulted from dietary or physiological factors and need to be examined further. © 2004 NRC Canada.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 science0.0000.001
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 head0.023
GPT teacher head0.333
Teacher spread0.310 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of International Crisis and Risk Communication ResearchSame topicMarine animal studies overviewFrench-language works237,207