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Record W2063021642 · doi:10.3138/jvme.35.4.496

Training Veterinary Students in Animal Behavior to Preserve the Human–Animal Bond

2008· article· en· W2063021642 on OpenAlexvenueno aff
Barbara L. Sherman, James A. Serpell

Bibliographic record

VenueJournal of Veterinary Medical Education · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfareVeterinary medicineAbandonment (legal)Animal-assisted therapyCurriculumCertificationCompanion animalPet therapyAnimal healthMedicinePsychologyBiologyManagementPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Knowledge of animal behavior is an extremely important component of modern veterinary practice. Appreciation of species-typical behavior helps to ensure that veterinary patients are handled safely and humanely, and plays a pivotal role in the diagnosis of health and welfare problems in animals, including the recognition of pain and distress. Veterinary students who acquire a good understanding of animal behavior will be better clinicians and will be best able to promote and repair the "human-animal bond," that important connection between people and their pets. Animal behavior problems can negatively impact this critical relationship, leading to abandonment, re-homing, relinquishment to an animal shelter, and sometimes premature euthanasia of the animal. Therefore, identifying, preventing, and treating behavior problems is important in maintaining the human-animal bond. Education in animal behavior should be an essential part of the veterinary curriculum; a board-certified veterinary behaviorist should be an integral member of the veterinary college faculty.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.427

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 integrity0.0000.000
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 head0.152
GPT teacher head0.488
Teacher spread0.336 · 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 designBench or experimental
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

Citations38
Published2008
Admission routes1
Has abstractyes

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