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Record W2607202284 · doi:10.1142/s0218127417500432

Multistable Phenomena Involving Equilibria and Periodic Motions in Predator–Prey Systems

2017· article· en· W2607202284 on OpenAlexafffund
Jiao Jiang, Pei Yu

Bibliographic record

VenueInternational Journal of Bifurcation and Chaos · 2017
Typearticle
Languageen
FieldMedicine
TopicMathematical and Theoretical Epidemiology and Ecology Models
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBistabilityHopf bifurcationLimit (mathematics)BifurcationLimit cycleBiological applications of bifurcation theoryMathematicsPitchfork bifurcationStatistical physicsStability (learning theory)Saddle-node bifurcationComplex dynamicsControl theory (sociology)Mathematical analysisApplied mathematicsPhysicsNonlinear systemComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

In this paper, we consider a number of predator–prey systems with various types of functional responses. Detailed analysis on the dynamics and bifurcations of the systems are given. Particular attention is focused on the complex dynamics due to bifurcation of limit cycles, which may generate bistable or tristable phenomena involving equilibria and oscillating motions. It is shown that predator–prey systems can exhibit such bistable or tristable phenomena due to Hopf bifurcation, giving rise to the coexistence of stable equilibria and stable periodic solutions. Explicit conditions on the system parameters are derived which can be used to determine the number of Hopf bifurcations, the stability of bifurcating limit cycles, and the parameter regime where the bistable or tristable phenomenon occurs. The method developed in this paper can be applied to study certain interesting patterns of complex dynamical behaviors in biological or other physical systems.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.038
GPT teacher head0.327
Teacher spread0.289 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

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