MétaCan
Menu
Back to cohort
Record W2001712847 · doi:10.3138/jvme.31.4.334

Emerging Challenges in Public Health Protection, Food Safety, and Security: Veterinary Needs in the USDA’s Food Safety and Inspection Service

2004· article· en· W2001712847 on OpenAlexvenueno aff
Bonnie Buntain

Bibliographic record

VenueJournal of Veterinary Medical Education · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsFood safetyHazard analysis and critical control pointsWorkforceBusinessAgency (philosophy)Public healthCritical control pointService (business)Veterinary public healthEnforcementEconomic shortageFood securityEnvironmental healthPublic relationsAgricultureMedicineMarketingGovernment (linguistics)NursingPolitical science

Abstract

fetched live from OpenAlex

Meeting the needs of public service practice is a responsibility of the veterinary profession. The United States Department of Agriculture (USDA) Food Safety and Inspection Service (FSIS) has undergone significant change since 1996, when the final rule on Pathogen Reduction and Hazard Analysis and Critical Control Point (HACCP) Systems and its regulations were published in response to food-borne illnesses and deaths due to E. coli 0157:H7 in undercooked hamburgers. As a result, the role of the veterinarian is changing from a focus on carcass inspection (reactive) to scientific-based systems analysis and enforcement (preventive). With a large pool of veterinarians eligible to retire, a critical shortage of field veterinarians is predicted. The purpose of this article is to raise educators' awareness of this need, of the competencies required, and of the challenges and opportunities for veterinarians in the new public health-focused FSIS. An invitation to collaborate with the agency is offered to help meet emerging workforce requirements in public health practice.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.158
GPT teacher head0.332
Teacher spread0.175 · 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 designOther design
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

Citations19
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Veterinary Medical EducationSame topicAnimal Disease Management and EpidemiologyFrench-language works237,207