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Record W2118821194 · doi:10.3201/eid1508.081061

Tactics and Economics of Wildlife Oral Rabies Vaccination, Canada and the United States

2009· review· en· W2118821194 on OpenAlexaboutno aff
Ray T. Sterner, Martin I. Meltzer, Stephanie A. Shwiff, Dennis Slate

Bibliographic record

VenueEmerging infectious diseases · 2009
Typereview
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsnot available
FundersNational Wildlife Research CenterU.S. Department of Agriculture
KeywordsRabiesWildlifeVaccinationPublic healthGeographyEnvironmental healthMedicineVirologyBiologyEcology

Abstract

fetched live from OpenAlex

Progressive elimination of rabies in wildlife has been a general strategy in Canada and the United States; common campaign tactics are trap-vaccinate-release (TVR), point infection control (PIC), and oral rabies vaccination (ORV). TVR and PIC are labor intensive and the most expensive tactics per unit area (approximately $616/km(2) [in 2008 Can$, converted from the reported $450/km(2) in 1991 Can$] and approximately $612/km(2) [$500/km(2) in 1999 Can$], respectively), but these tactics have proven crucial to elimination of raccoon rabies in Canada and to maintenance of ORV zones for preventing the spread of raccoon rabies in the United States. Economic assessments have shown that during rabies epizootics, costs of human postexposure prophylaxis, pet vaccination, public health, and animal control spike. Modeling studies, involving diverse assumptions, have shown that ORV programs can be cost-efficient and yield benefit:cost ratios >1.0.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.441
Threshold uncertainty score0.887

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.259
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations105
Published2009
Admission routes1
Has abstractyes

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