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Record W3176307900 · doi:10.1080/11956860.2021.1916215

Understanding rabies persistence in low-density fox populations

2021· article· en· W3176307900 on OpenAlexafffundvenue
E. Joe Moran, Nicolas Lecomte, Patrick A. Leighton, Amy Hurford

Bibliographic record

VenueEcoscience · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsUniversité de MontréalCenter for Northern StudiesUniversité de MonctonMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRabiesVulpesLagopusArctic foxCarrying capacityArcticEcologyGeographyTundraBiologyPredationVirology

Abstract

fetched live from OpenAlex

Arctic fox (Vulpes lagopus) and its tundra habitat are a unique system for the study of rabies virus epidemics. Contrary to theoretical calculations reporting a critical density (KT) of approximately 1 fox/km2 for rabies endemicity, arctic rabies persists at densities below this. The calculation of KT = 1 fox/km2 assumes uniform fox density across the landscape and unrestricted mixing between susceptible and infected foxes. We hypothesize that spatial heterogeneity arising from resource distribution or social structure may result in regions where rabies is endemic, even though average fox densities at the regional or landscape-level are below KT. To expand upon the limited body of research surrounding arctic rabies persistence, we examine arctic rabies via a two-patch structure. We find that arctic rabies can persist in heterogeneous landscapes where the mean carrying capacity is below the threshold carrying capacity required for endemicity in homogeneous landscapes. Rabies endemicity in low-carrying capacity regions within heterogeneous landscapes is further facilitated by high transmission rates, potentially due to non-breeding foxes (i.e. floaters), and when between-patch movement is restricted to latent and infected foxes. Our results suggest that rabies may persist in heterogeneous landscapes when the mean carrying capacity is as low as 0.25 foxes/km2.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.307
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.172
GPT teacher head0.284
Teacher spread0.112 · 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 designObservational
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

Citations9
Published2021
Admission routes3
Has abstractyes

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