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Record W2112414377 · doi:10.1002/ar.20565

Evaluation of postmortem changes in tissue structure in the bottlenose dolphin (<i>Tursiops truncatus</i>)

2007· article· en· W2112414377 on OpenAlexaff
Megan F. McKenna, Jeremy A. Goldbogen, Judy St. Leger, John A. Hildebrand, Ted W. Cranford

Bibliographic record

VenueThe Anatomical Record · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
FundersU.S. Navy
KeywordsBottlenose dolphinBiologyAnatomyFishery

Abstract

fetched live from OpenAlex

Postmortem changes in geometry, density, and sound speed within organs and tissues (melon, bone, blubber, and mandibular fat) of the dolphin head were evaluated using computed tomography (CT) scans of live and postmortem bottlenose dolphins (Tursiops truncatus). Specimens were classified into three different treatment groups: live, recently dead, and frozen followed by thawing. Organs and tissues in similar anatomical regions of the head were compared in CT scans of the specimens to identify postmortem changes in morphology. In addition, comparisons of Hounsfield units in the CT scans were used to evaluate postmortem changes in the density of melon, bone, blubber, and mandibular fat. Sound speed measurements from melon, blubber, connective tissue, and muscle were collected from fresh and frozen samples in the same specimen to evaluate effects due to freezing and thawing process on sound speed measurements. Similar results in tissue and organ geometry, density, and sound speed measurements suggested that postmortem material is a reliable approximation for live melon, bone, blubber, muscle, connective tissue, and mandibular fat. These results have implications for examining viscoelastic properties and the accuracy of simulating sound transmission in postmortem material.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.301
Teacher spread0.275 · 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

Citations36
Published2007
Admission routes1
Has abstractyes

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