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Record W2003750775 · doi:10.3934/dcdsb.2012.17.2829

Avian influenza dynamics in wild birds with bird mobility and spatial heterogeneous environment

2012· article· en· W2003750775 on OpenAlexafffund
Naveen K. Vaidya, Feng‐Bin Wang, Xingfu Zou

Bibliographic record

VenueDiscrete and Continuous Dynamical Systems - B · 2012
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research Chairs
KeywordsEigenvalues and eigenvectorsInfluenza A virus subtype H5N1HomogeneousDynamics (music)Principal (computer security)Constant (computer programming)Biological systemEcologyStatistical physicsBiologyComputer sciencePhysicsVirology

Abstract

fetched live from OpenAlex

In this paper, we propose a mathematical model to describe the avianinfluenza dynamics in wild birds with bird mobility andheterogeneous environment incorporated. In addition to establishingthe basic properties of solutions to the model, we also prove thethreshold dynamics which can be expressed either by the basicreproductive number or by the principal eigenvalue of thelinearization at the disease free equilibrium. When the environmentfactor in the model becomes a constant (homogeneous environment), weare able to find explicit formulas for the basic reproductivenumber and the principal eigenvalue. We also perform numericalsimulation to explore the impact of the heterogeneous environment onthe disease dynamics. Our analytical and numerical results revealthat the avian influenza dynamics in wild birds is highly affectedby both bird mobility and environmental heterogeneity.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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 designSimulation or modeling
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

Citations46
Published2012
Admission routes2
Has abstractyes

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