MétaCan
Menu
Back to cohort
Record W4392247007 · doi:10.1016/j.aml.2024.109041

Stationary distribution analysis of a stochastic SIAM epidemic model with Ornstein–Uhlenbeck process and media coverage

2024· article· en· W4392247007 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueApplied Mathematics Letters · 2024
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsYork University
FundersNatural Science Foundation of Hebei ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsOrnstein–Uhlenbeck processMathematicsStationary distributionJumpStochastic processApplied mathematicsDistribution (mathematics)Lyapunov functionNonlinear systemEpidemic modelMean reversionEconometricsMathematical analysisStatisticsMarkov chainPopulation

Abstract

fetched live from OpenAlex

A stochastic SIAM (Susceptible individual-Infected individual-Aware individual-Media coverage) epidemic model with nonlinear disturbances is constructed, where awareness dissemination rate satisfies the mean-reverting Ornstein–Uhlenbeck process. Hybrid dynamic effects of Lévy jump and Ornstein–Uhlenbeck process on infectious disease transmission are discussed. By constructing appropriate stochastic Lyapunov functionals, sufficient conditions for existence of stationary distribution of the proposed stochastic system are investigated.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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