MétaCan
Menu
Back to cohort
Record W7098331247

National Wildlife Health Center Sylvatic Plague Vaccine and Management of Prairie Dogs

2013· article· en· W7098331247 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPlague (disease)WildlifePrairie dogOutbreakWildlife diseaseYersinia pestisCynomys ludovicianusWildlife management
DOInot available

Abstract

fetched live from OpenAlex

the University of Wisconsin (UW), have developed a sylvatic plague vaccine that shows great promise in protecting prairie dogs against plague (Mencher and others, 2004; Rocke and others, 2010). Four species of prairie dogs reside in the United States and Canada, and all are highly susceptible to plague and regularly experience outbreaks with devastating losses. Along with habitat loss and poisoning, plague has contributed to a significant historical decline in prairie dog populations. By some estimates, prairie dogs now occupy only 1 to 2 percent of their former range (Proctor and others, 2006), with prairie dog colonies being now much smaller and fragmented than they were historically, making individual colonies more vulnerable to elimination by plague (Antolin and others, 2002). At least one species, the Utah prairie dog (Cynomys parvidens) is listed by the U.S. Fish and Wildlife Service (FWS) as “threatened”. Controlling plague is a vital concern for ongoing management and conservation efforts for prairie dogs. Sylvatic Plague: A Continued Threat to North American Wildlife Sylvatic plague, caused by Yersinia pestis, is a bacterial disease transmitted by fleas that afflicts many mammalian species, including humans. For many species of wildlife, plague mortality is a serious conservation issue. In fact, more than half of North American rodent species considered of

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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
GenreOther

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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicPolar Research and EcologyFrench-language works237,207