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Record W2148023333 · doi:10.3138/jvme.33.3.401

Zoo and Wildlife Medical Education: A European Perspective

2006· article· en· W2148023333 on OpenAlexvenueno aff
Kai Frölich, Susanne E Grabitzky, Chris Walzer, Richard J. Delahay, Gerry M. Dorrestein, Jean‐Michel Hatt

Bibliographic record

VenueJournal of Veterinary Medical Education · 2006
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeErasmus+Veterinary medicineMedical educationMedicinePolitical scienceBiologyEcology

Abstract

fetched live from OpenAlex

Europe has a long and distinguished history in veterinary science and education, and it was here that the first professional investigations of pathological conditions in zoo animals took place. However, despite an increasing number of veterinarians working with wildlife, education in zoological and wildlife medicine has only recently become part of formal veterinary training at the undergraduate level. Consequently, current educational opportunities in zoological and wildlife medicine vary widely throughout Europe, both in availability and in composition. The need to establish agreed standards in education across Europe and to foster the mobility of students and teaching staff are reflected by international agreements such as the Bologna Treaty and the ERASMUS-SOCRATES program. Europe is also home to a number of voluntary professional organizations, such as the European Wildlife Disease Association and the European Association of Zoo and Wildlife Veterinarians, that actively promote the inclusion of zoological and wildlife medicine in veterinary education. Zoo and wildlife medicine is currently a veterinary specialization in Europe, but educational opportunities are likely to increase in availability in the future.

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)0.007
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.221
GPT teacher head0.531
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.

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

Citations8
Published2006
Admission routes1
Has abstractyes

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