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Record W4212803979 · doi:10.1093/ilar/ilab030

Balancing Animal Welfare, Human Safety, and Research in Agriculture High Containment

2020· article· en· W4212803979 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueILAR Journal · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsCanadian Science Centre for Human and Animal HealthCanadian Food Inspection Agency
Fundersnot available
KeywordsAgricultureLivestockBusinessBiosafetyAnimal welfareAnimal agricultureContainment (computer programming)Work (physics)Food securityHuman healthPublic healthEnvironmental planningPolitical scienceEnvironmental healthBiotechnologyMedicineEngineeringComputer scienceEnvironmental scienceGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Livestock research is paramount to understanding the risks associated with unintentional and intentional introductions of emerging, reemerging, and transboundary animal diseases, including their relationship to both the security of the economy and the nation’s food supply. Research involving large animal species conducted in maximum containment Biosafety Level (BSL)-3Ag and BSL-4 facilities include Health and Human Services and United States Department of Agriculture Select Agents that can have severe consequences on both animal agricultural industry and public health. This themed issue of ILAR spans the spectrum of concerns related to this special niche within the animal research community with an emphasis on a review of available research, current trends, and novel approaches relevant to those conducting large animal research with high-risk agents and those charged with regulating those facilities and programs. Articles are authored by those embedded in the high- and maximum-containment community, directly involved with the work, detailing the unique challenges associated with BSL-3 and BSL-4 livestock research.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.056
GPT teacher head0.297
Teacher spread0.241 · 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