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Record W2790824128 · doi:10.1111/vru.12599

Ultrasonographic anatomy of reproductive female leopard geckos (<i>Eublepharis macularius</i>)

2018· article· en· W2790824128 on OpenAlexafffund
Ophélie Cojean, Claire Vergneau‐Grosset, Isabelle Masseau

Bibliographic record

VenueVeterinary Radiology & Ultrasound · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsUniversité de Montréal
FundersUniversité de Montréal
KeywordsAnatomyMedicineCoelom

Abstract

fetched live from OpenAlex

Captive leopard geckos (Eublepharis macularius) often present to the exotic clinic for gastrointestinal impactions, follicular stasis, or dystocia. To our knowledge, normal ultrasonographic anatomy of these lizards has not been described. The objectives of this prospective, anatomic, analytical study were to develop ultrasound techniques for this species and to describe the normal sonographic anatomy of the head, coelomic cavity, and tail. Eleven, healthy, female leopard geckos were included. A linear array 13-18 MHz transducer was used. Geckos were sedated and restrained in dorsal recumbency for coelomic structure examination and in ventral recumbency for head and tail examinations. Sagittal and transverse images were acquired and authors recorded qualitative and quantitative ultrasonographic characteristics of anatomic structures. The ventral surface of the lungs, liver, gallbladder, caudal vena cava, portal vein, ventral abdominal vein, aorta, ovarian follicles, fat bodies, tail, and brain were visualized in 10 of 11 individuals. In one individual, molt precluded ultrasonographic examination. The heart, kidneys, urinary bladder, spleen, and pancreas were not visualized. The digestive tract was observed in 10 individuals but was too small to be measured. Findings from the current study could be used as a reference for future studies of leopard geckos.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient 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 score0.570
Threshold uncertainty score1.000

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 studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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