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Record W2790923560 · doi:10.14745/ccdr.v42i06a01

The changing face of rabies in Canada

2016· editorial· en· W2790923560 on OpenAlexaffvenueabout
Catherine Filejski

Bibliographic record

VenueCanada Communicable Disease Report · 2016
Typeeditorial
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsMinistry of Health and Long Term Care
Fundersnot available
KeywordsRabiesRabies virusCommunicable diseaseDisease controlGeographyPolitical scienceEnvironmental planningMedicineEnvironmental healthVirologyPublic health

Abstract

fetched live from OpenAlex

Rabies prevention and control programs in Canada have proven highly successful in past decades and have significantly reduced both terrestrial animal and human rabies cases.Successful management and prevention of rabies to date have not, however, eliminated our need for ongoing rabies prevention and control programs.This issue of the Canadian Communicable Disease Report (CCDR) provides an overview of recent and emerging rabies trends and challenges in Canada and examines the rationale to maintain our rabies programs and further supplement them with new and innovative approaches.The articles in this issue cover a broad range of topics including the preparation for, and response to, renewed incursions of the raccoon rabies variant of the virus, how to address the problem posed by the movement of dogs from northern to southern Canada and how the Canadian Rabies Management Plan is being revised and updated to respond to these issues.Rabies in Canada is changing, but it is not disappearing.The same needs to be said of our rabies prevention and control policies and programs.

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)0.006
metaresearch head score (Gemma)0.018
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.588
Threshold uncertainty score0.819

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.002
Science and technology studies0.0060.005
Scholarly communication0.0100.004
Open science0.0040.002
Research integrity0.0150.024
Insufficient payload (model declined to judge)0.0050.002

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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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
GenreEditorial

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

Citations13
Published2016
Admission routes3
Has abstractyes

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