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Record W2050976497 · doi:10.3138/jvme.33.3.331

Current Perspectives on Curriculum Needs in Zoological Medicine

2006· article· en· W2050976497 on OpenAlexvenueaboutno aff
Michael K. Stoskopf

Bibliographic record

VenueJournal of Veterinary Medical Education · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsnot available
Fundersnot available
KeywordsExcellenceCurriculumVeterinary public healthPublic healthAnimal healthWhite paperLivestockWildlifeMedical educationMedicineVeterinary medicinePolitical scienceGeographyNursingBiology

Abstract

fetched live from OpenAlex

Advances have been made in expanding veterinary curricula to deliver basic key knowledge and skills necessary for provision of health care to captive and companion non-domestic or non-traditional species in the veterinary colleges of the United States and Canada. These advances were in large part facilitated by the deliberations and recommendations of the White Oak Accords. Though a five-year review of curricular opportunities at US and Canadian veterinary colleges shows that progress has been made in implementing the recommendations of the White Oak Accords, there remains room for improvement. The broadly comparative and health-maintenance basis of zoological medicine contributes critically to the potential for veterinary medicine to make important contributions to the concept of the integrated health of the planet. Emergence of key zoonotic and production-animal diseases derived from and within wildlife populations since 2000 has increased awareness worldwide of the importance of zoological medicine in protecting both production livestock and public health. These areas are addressed in elective curricula at colleges emerging as centers of excellence in zoological medicine, but it is critical that core curricula in zoological medicine at all schools be strengthened to include these important areas to prepare our DVM/VMD graduates to protect companion-animal, production-animal, and public health.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.378
Teacher spread0.339 · 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 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

Citations7
Published2006
Admission routes2
Has abstractyes

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