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Record W2095168124 · doi:10.3138/jvme.32.3.377

The North Carolina State University College of Veterinary Medicine Turtle Rescue Team: A Model for a Successful Wild-Reptile Clinic

2005· article· en· W2095168124 on OpenAlexvenueno aff
Gregory A. Lewbart, Jennifer Kishimori, Larry S. Christian

Bibliographic record

VenueJournal of Veterinary Medical Education · 2005
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
FundersNorth Carolina State UniversityUniversity of Pennsylvania
KeywordsTurtle (robot)WildlifeVeterinary medicineService (business)MedicineWork (physics)Family medicineMedical educationEcologyBiologyBusinessEngineering

Abstract

fetched live from OpenAlex

The North Carolina State University College of Veterinary Medicine (NCSU-CVM) Turtle Rescue Team (TRT) is a veterinary student-run organization that treats native, sick and injured, wild chelonians. First-, second-, and third-year students are responsible for case management, consultation coordination, diagnostic testing within the hospital, and placing of recuperating animals with local wildlife rehabilitators. Several clinical research publications have resulted from the opportunity to work with these wild reptiles. Active student participants can also gain a course credit by attending eight hours of lecture/ seminar related to reptile medicine. With regards to outcome assessment, 86% of survey respondents found the program to be valuable or extremely valuable to their veterinary medical education. The logistics of organizing, supporting, and running this service are discussed, and its value as a clinical learning tool is supported by the results of a survey.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.265
GPT teacher head0.505
Teacher spread0.239 · 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 designNot applicable
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
Published2005
Admission routes1
Has abstractyes

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